# Human Activity Recognition in an Open World

Derek S. Prijatelj<EMAIL_ADDRESS>
Samuel Grieggs<EMAIL_ADDRESS>
Jin Huang<EMAIL_ADDRESS>
Department of Computer Science and Engineering
University of Notre Dame
Notre Dame, IN 46556, USA
Dawei Du<EMAIL_ADDRESS>
Ameya Shringi<EMAIL_ADDRESS>
Christopher Funk<EMAIL_ADDRESS>
Kitware, Inc.
1712 Route 9, Suite 300, Clifton Park, NY 12065, USA
Adam Kaufman<EMAIL_ADDRESS>
Eric Robertson<EMAIL_ADDRESS>
PAR Government
421 Ridge St, Rome, NY 13440, USA
Walter J. Scheirer<EMAIL_ADDRESS>
Department of Computer Science and Engineering
University of Notre Dame
Notre Dame, IN 46556, USA

###### Abstract

Managing novelty in perception-based human activity recognition (HAR) is
critical in realistic settings to improve task performance over time and
ensure solution generalization outside of prior seen samples. Novelty
manifests in HAR as unseen samples, activities, objects, environments, and
sensor changes, among other ways. Novelty may be task-relevant, such as a new
class or new features, or task-irrelevant resulting in nuisance novelty, such
as never before seen noise, blur, or distorted video recordings. To perform
HAR optimally, algorithmic solutions must be tolerant to nuisance novelty, and
learn over time in the face of novelty. This paper 1) formalizes the
definition of novelty in HAR building upon the prior definition of novelty in
classification tasks, 2) proposes an incremental open world learning (OWL)
protocol and applies it to the Kinetics datasets to generate a new benchmark
KOWL-718, 3) analyzes the performance of current state-of-the-art HAR models
when novelty is introduced over time, 4) provides a containerized and packaged
pipeline for reproducing the OWL protocol and for modifying for any future
updates to Kinetics. The experimental analysis includes an ablation study of
how the different models perform under various conditions as annotated by
Kinetics-AVA. The protocol as an algorithm for reproducing experiments using
the KOWL-718 benchmark will be publicly released with code and containers at
https://github.com/prijatelj/human-activity-recognition-in-an-open-world. The
code may be used to analyze different annotations and subsets of the Kinetics
datasets in an incremental open world fashion, as well as be extended as
further updates to Kinetics are released.

Figure 1:  An example of open world visual human activity recognition starting
from Kinetics-400 (?) known activities and incrementally learning novel
activities from Kinetics-700-2020 (?, ?). In Increment 2, without handling
novelty, all unknown activities would be misclassified, when it is more
correct to label them as “unknown.” In an open world, learning unknown
activities is desired, as in Increment 3 where “ironing hair” and “blowdrying
hair” have been learned by the predictor and represented with stand-in unknown
classes 1 and 2 to differentiate from the general unknown class. These stand-
in labels remain until the predictor is given (or determines) a human readable
label.

##  1 Introduction

Human activities consist of a multitude of visual phenomena whose mappings to
semantic classes are noisy and uncertain. These activities may vary from
swimming alone in the ocean, to playing in team sports such as football, or to
reading an academic article. Not only can the activities be different, but the
environments in which they take place may be highly varied (?). The task of
Human Activity Recognition (HAR) consists of recognizing and classifying these
activities in visual recordings (?). Given the extent of possible human
activities and the variation within a single activity, datasets are often
under-sampled representations of the task, which results in a problem of
generalization for the predictors that perform HAR when they encounter novel
human activities or even novel instances of known human activities. This
necessitates learning over time as more data is released and serves as a
practical example of incremental open world learning.

The Open World Learning (OWL) setting (?, ?, ?) is designed to expect novelty,
including the lack of exhaustive samples for known classes and the lack of any
samples for unseen, novel classes. As such, OWL presents a more practical
experimental setting for HAR that has yet to be introduced in the HAR
literature. OWL may take on the form of incremental learning (?) where new
batches of samples are made available over the time-steps. The task is to
perform HAR and learn the activity classes over time as new samples are
introduced. An instantiation of HAR can be found in the Kinetics datasets,
which have been released since 2017, introducing new classes and samples,
including Kinetics-400 (?), Kinetics-600 (?), Kinetics-700-2020 (?, ?), and
Kinetics-AVA (?). For brevity, if we refer to Kinetics-700, we mean the 2020
updated version. Ideally, HAR predictors should be comparable and generalize
across the Kinetics datasets. Given the inherent lack of samples to
characterize all human activities, HAR predictors must account for learning
over time as data is made available, as in the case of the Kinetics and other
datasets, and these predictors should adapt to novelties that affect task
performance (?). To evaluate such predictors, we define an OWL protocol with
accompanying code to configure and run a Kinetics Open World Learning (KOWL)
experiment.

Contributions: To better evaluate the generalization of predictors in Open
World HAR when encountering novelty, this paper introduces:

  1. 1.

A formal definition of Open World HAR in Section 3 as an instantiation of the
newly introduced framework by (?), which establishes a common language for
novelty across AI domains;

  2. 2.

An OWL protocol in Section 4 as an extension to (?) to create experiments to
analyze novelty in HAR datasets, applied to Kinetics 400 (?), 600 (?), and
700-2020 (?) to create KOWL-718, accompanied by baseline predictors for
benchmarking;

  3. 3.

An detailed analysis in Section 5 of the performance of pre-existing state-of-
the-art visual HAR models in the presence of novelty, including X3D (?) and
TimeSformer (?).

  4. 4.

Pipeline code for configuring and running the open world HAR experiments111
Code repository with containers for reproducing and extending:
https://github.com/prijatelj/human-activity-recognition-in-an-open-world with
a Docker image to both reproduce the Kinetics experiments below, as well as
enable any configuration for open world Kinetics experiments in the future.

##  2 Background and Related Work

Fundamentally, novelty is anything that is new to something else. This could
be related to the task being learned, such as a sample of a new class in the
evaluation set, or new features that correlate strongly with a known class
(?). Such things are deemed “task-relevant” if they contain information to
accomplish the task (?, ?). Novelty can also be task-irrelevant, for example
the color of one’s new clothes is a feature of a video that should be mostly
uncorrelated with the activity of standing in place. Such task-irrelevant
novelty is termed “nuisance novelty” and its affect on task performance is to
be minimized. Such concepts of novelty have been defined in a formal
abstraction with a relation to agents performing their tasks in general AI
settings by (?). Note that (?) define their abstraction referring to the
task’s algorithmic solution as an “agent,” where in this work we refer to them
as “predictors.” The primary actions of the predictors observed in this work
are their predicted classification probability vectors and an optional
feedback request through the ordering of the sample identifiers based on
priority of interest to the predictor.

Open Set Recognition (?, ?), Anomaly Detection (?, ?), Novelty Detection (?),
and Out-of-Distribution Detection (?) are all intended to detect when a sample
occurs that contains novel information that was not represented by the prior
data. For simplicity, this paper refers to these similar tasks solely as
“novelty detection.” In terms of classification tasks, as in Open Set
Recognition, a sample with a large amount of novel information cannot be
reliably classified as a known class, and instead should be labeled as an
unknown class. Novelty detection does not involve incremental learning to
adapt to novelty; it only involves the detection of novelty. This may be seen
as detecting out-of-distribution samples based on a single time-step.

OWL (?, ?, ?) is an example of learning-based classification or recognition
with novelty in mind. OWL is the natural extension of novelty detection where
new classes are learned alongside the known classes. Recent work has proposed
a practical OWL protocol for image classification (?) as an extension of
Incremental Classifier and Representation Learning (?), but no OWL protocol,
benchmark dataset, or model exist for HAR. In this paper we extend OWL to HAR,
a more visually complicated problem than static image classification. Our work
contains multiple key differences from this prior work. The protocol we
introduce below measures performance using a confusion matrix per time-step,
which captures all the information for a single time-step between the
predictor’s nominal output as the maximally likely activity class. Dhamija et
al. only observe accuracy. In Section 5 we demonstrate why observing only
accuracy as the single point-estimate measure of performance at a time-step is
insufficient. Furthermore, Dhamija et al. only observe classification
performance for known classes alone, known classes with a catch-all unknown
class for others, and binary classification of novelty detection. Our protocol
encourages looking at not only novelty detection, but also novelty
recognition, which is the assessment of the predicted unknown class-clusters
to define new classes that can be considered known information going forward.
Incremental OWL is inherently a semi-supervised task and it is important to
evaluate the quality of recognized unknown classes. Kinetics has been
typically assessed using top-5 accuracy. Given this, our approach considers
the top-$k$ ordered confusion matrices in which one may obtain the top-$k$
accuracy, along with the top-1 confusion matrix and its measures derived from
it, as we do throughout this paper.

While the confusion matrix covers task performance at a single time-step (non-
cumulative) or over multiple increments (cumulative), Dhamija et al. do not
evaluate the reaction time of the predictor to novelty occurring. We include
details of a way to do so in Section 4.1. In addition, we also include the
action predictors may take, the feedback request as explained in Section 4.2.
Requested feedback may be limited in practice due to the cost of humans,
especially human experts, annotating the samples. Due to this, we included an
any % feedback experiment parameter where the evaluator limits the amount of
labels the predictor may receive for their training set of the new increment.
Given this limitation, the predictor is forced to order the samples it
received within the current increment’s training set. This becomes a subtask
to request the labels that enable more efficient learning of the task.

Another important distinction between the work of Dhamija et al. and our work
is that we encourage the predictors to have no prior knowledge when being
evaluated on this baseline, or at least to have an explicit disclaimer of
their prior knowledge such that those predictors may be properly compared to
similarly prior informed predictors. For this benchmark to assess the learning
of this task as represented by this data, no prior knowledge should be used.
Otherwise, the assessment is more about whether the prior knowledge shared
information with this task or better enabled the learning of this task. This
distinction is important, especially considering the current dominant trend in
AI of using large pre-trained models for transfer learning to down-stream
tasks (?).

There are some HAR works related to Open Set Recognition (?, ?, ?, ?), but
none that attempt to provide a benchmark or solution for OWL in HAR, which
involves incremental learning in a world with unknown human activity classes
and their associated videos. Three methods approach this goal. Deep Metric
Learning for Open Set HAR (?) uses metric learning to improve feature
representation models for open set recognition, but the associated experiments
do not involve incremental learning. Activity Explorer (?) is described as
having an ability to recognize potentially new classes, but is only assessed
within an OSR context, rather than in an OWL context. It does not include
logic on how to update classes over time, leaving it to the human user.
OSVidCap (?) is for the task of Video Captioning, which is related to HAR, but
is not HAR specifically. Like the other two approaches, it is evaluated in an
Open Set Recognition mode, not OWL.

A feature representation space that generalizes to the task and captures the
world’s task-relevant information is an important part in solving HAR with
novelty. The state of representation learning consists of large semi-
supervised deep neural networks with copious amounts of data (?, ?), or
generative models that leverage adversarial examples or augmentations for
contrastive learning (?). Feature representation is important for OWL because
it determines what information is available to learn the task for the down-
stream components, such as the classifier (see Figure 6). Not only do OWL
classifiers need to be able to learn new classes over time, but ideally their
representation learners must also be able to adapt to include missing task-
relevant information. This is similar to the popular pretrained models in
natural language processing where a semi-supervised model, such as a
transformer, is pretrained on an upstream task with a large dataset and in
doing so learns information relevant to many downstream tasks (?). With a
feature representation that contains information not only relevant to the
current downstream task but also contains possibly future relevant task
information, the classifiers are able to distinguish between known and
unknown, learning novel classes over time. Incremental learning model types
that perform fine-tuning are one way to adapt the feature representation space
over the increments (?). Frozen or fixed representations rely on a feature
representation that already contains the information relevant to learning
novel classes in the future, where any downstream fine-tuning or classifier
changes will change their learned function to use task-relevant information in
the feature representation ignored prior to the novel class being introduced,
given the data processing inequality (?).

##  3 Formalization of Open World HAR

(?) provided a convenient framework for formalizing novelty problems in AI. A
formal definition of novelty for HAR provides a standardized way to
communicate and understand the impact of novelty on the performance of a
learning-based system meant to recognize activities. This enables informed
discussion about the nuances of OWL, including new classes and new types of
task-relevant phenomena such as new visual features, e.g., associating the
visual depiction of a football with the sport.

The formalization starts with defining a task. HAR follows the classification
task definition of learning a mapping
$f\in\\{F:\mathrsfso{X}\rightarrow\mathrsfso{Y}\\}$ that is some deterministic
function of the possible mappings of the space of possible visuals of human
activity $\mathrsfso{X}$ to the space of possible human activity classes
$\mathrsfso{Y}$. This mapping is estimated by a predictor from paired data
samples $(x,y)$: a video sample $x\in\mathrsfso{X}$ and the most likely human
activity class label $y\in\mathrsfso{Y}$.

As noted earlier, it is well understood that the sampling of videos is often
incomplete, but hopefully is representative of a subset of the possible
videos. Given the space of class labels $y\in\mathrsfso{Y}$ is never complete
in OWL, novel activity classes thus are possible in future encountered HAR
video samples. As such, the learning of an optimal mapping of $f$ from data
persists over time in OWL where one of the class labels stands for the unknown
classes. This unknown class is used to represent the detection of novel
classes. A HAR predictor’s output space or prediction space
$\mathcal{{\hat{Y}}}$ overlaps with the class space $\mathrsfso{Y}$.

In addition to this task, let there be novelty spaces in which the phenomena
of the task occur. A novelty space includes:

  * •

State Space: A measurable space in which the states $s\in\mathcal{S}$
associated with various activities occur.

  * •

Dissimilarity Measure: The quantification of how different states are to one
another, possibly with a threshold for binary dissimilarity decisions and
possibly task-specific to ignore some task-irrelevant phenomena.

  * •

Regret Measure: The quantification of how undesirable a predictor’s
${\hat{Y}}$ decision or the resulting state of that decision is relative to
the task.

  * •

Experience: A time-indexed history of the states that have occurred over time.

  * •

State Transition Function: a function that moves from the current state to the
next state given a change in time or an action taken by the predictor. In HAR,
the time-step $t$ corresponds to the index of the current sample and the
predictor decides the class, detects novelty, possibly updates its state, and,
if possible, may request feedback.

Novelty within this space is then determined by a new state that is
significantly dissimilar based on the the Dissimilarity Measure to the states
recorded in the prior Experience. Following (?), HAR may be defined with three
novelty spaces: 1) World space, 2) Observation space, and 3) Predictor space.
Examples of the different subsets of novelty associated with these spaces are
provided in Appendix B.

Our paper primarily focuses on the spaces in which novelty may occur related
to classifying human activities, all of which were mentioned earlier: the
input space $\mathrsfso{X}$, the predictor output space $\mathcal{{\hat{Y}}}$,
and the ground truth target label space $\mathrsfso{Y}$ obtained from a
dataset. The relation of these to the primary three is as follows:

  * •

The world defines that which is considered the truth to be learned, as defined
by the dataset, meaning the paired mapping of video $x$ to ground truth label
$y$. The dataset as in order of presentation to the predictor forms the world
experience. The order of presentation is based on the chosen experimental
design and serves as the state transition function of the observed world. The
regret measures are based on the relative comparison of predictions to the
ground truth labels. Section 4.1 goes into explicit detail for any KOWL
experiment. In this paper, all of these parts are controlled and executed by
the “evaluator,” as seen in Figure 2, which is the algorithmic execution of
the experiment.

  * •

The observation space relates to how these human activities were captured
visually, such as camera sensors. The original dataset determines the source
of the visual recordings and may include any metadata defining those
recordings. This is where nuisance novelty occurs, including never before
experienced perspective rotations, hue shifts, blurs and noise.

  * •

The predictor space consists of the predictor’s output space, experience, and
internal state. Internal regret measures are those such as the optimization
function in which the predictor optimizes as it learns, such as losses for
neural networks. State transition functions for the predictor are its update
functions as it receives videos and any feedback from the evaluator. See
Section 4.4 for the complete abstraction and Section 5.1 for examples.

More detailed explanations of the world, observation, and predictor spaces as
structured in (?) for defining novelty in HAR are available in the Appendices
A and B.

Figure 2:  A single $n$-th increment of the OWL protocol depicting the
exchange of data, state, and updating internal state for both the evaluator
and the predictor. An increment consists of a pre-feedback phase (whole step
$n$) and a post-feedback phase (half step $n+0.5$), if any feedback given. On
the $n$-th increment, new sample data is given from the evaluator to the
predictor without any labels. The predictor performs any internal updates
given this new unlabeled data, such as un- or semi-supervised learning, and
returns its predictions. The predictor also returns the order of the samples
it would like to receive feedback for. The evaluator evaluates the pre-
feedback predictions and if it gives any feedback will note the samples whose
feedback were given in its experience. In the case of giving ground truth
class labels, those labels are now deemed as known. The predictor may update
its state given any feedback it receives and perform a post-feedback
prediction, which is then evaluated again. The process repeats until the
experiment’s increments are exhausted. At the initial increment, prior
knowledge obtained from external data, such as feature representations from
other models, needs to be explicitly stated and evaluated appropriately. See
Section 4.4.

###  3.1 Nuisance Novelty

It is important in all learning tasks to maximize the task-relevant
information in the predictions and filter out the task-irrelevant information
(?), as measured by correlation measures of the predictions to the ground
truth labels. Task-irrelevant information is noise or other irrelevant
phenomena that occurs in the world and is possibly captured by the sensor.
Nuisance novelties are novel task-irrelevant phenomena that have never before
been encountered and which the predictor is to be tolerant to and maintain
task performance. An example in HAR is that if someone is dancing, then in
general their clothes should not be a strongly correlated feature to the
recognized activity of dancing for the predictor to generalize across dancing
videos. Nuisance novelty is defined in (?) as a novelty for which a pair of
regret measures significantly disagree. In this example, the predictor may
falsely detect novelty with respect to a new activity class when in reality it
is only someone dancing in a never-before-seen colored shirt and the activity
class label is still dancing.

Often is the case that some novelty may occur in the world, such as a sensor
being knocked over and the visual input being rotated. The input being rotated
is novel if rotations were never seen in training, but if all of the actions
being recorded are known, then the predictor is desired to be tolerant to
rotations of its field of view. However, if this rotation causes the predictor
to continuously detect novel classes, despite them being known, then this is a
nuisance novelty as the desired world regret pertaining to the HAR task is low
while the predictor’s regret is high as it deems task-irrelevant information
as task-relevant. Predictors need to maintain task performance in the face of
such nuisance novelties. Further explanations of the different types of
novelty with human activity are provided in appendix B.

##  4 Open World Learning Protocol for HAR

Any labeled HAR dataset may be partitioned in such a way that yields an OWL
HAR dataset with increments that consist of videos with shared known
activities and novel activities. The following focuses on forming increments
on the training data split. The validation and testing data splits may be
split in the same way using the same class sets per increment such that they
are aligned to the training data. Given the number of desired increments $N$,
a labeled dataset that is an unordered sequence of pairs $(x,y)$, and the
starting known label set $K\subset\mathrsfso{Y}$, the starting unknown label
set $U$ within the data is then the unique labels not contained within $K$,
where $U=\\{y:y\notin K\\}$. The pairs with known labels may be split into the
$n$ increments in any way desired, such as stratified splits that maintain the
balance of the known labels across the increments as much as possible with
finite, discrete samples. With the known labels partitioned, the increments
must also introduce novel classes when encountered. To do so, the unknown
labels are separated uniformly across the $N$ increments such that there are
$\lfloor\frac{|U|}{N}\rfloor$ unknown classes within in the first $N-1$
increments and the remaining unknown classes go into the final increment. At
this point, every increment shares the known classes and has a subset of
unknown classes disjoint to all other increments.

Given that we want the novel classes introduced at each increment to remain
present in future increments, each increment’s unknown class samples need to
be spread across the remaining increments that follow in time. To counteract
class imbalance, it is useful to order the unknown classes by descending order
of sample frequency and insert them as the most frequent unknown class first
over the time ordered increments. The increment at time-step $t$ has its
unknown label set $U_{t}$ that is stratified split into $n-t$ splits. One of
those splits remains in the current $t$ increment. The other $n-t-1$ of those
splits are added to the remaining future increments, where
$t\in\\{0,1,...,n-2,n-1\\}$. This results in an OWL HAR dataset that can be
used to train and evaluate a predictor incrementally where sample index
corresponds to time and there are $N$ class distributional changes where novel
classes are introduced, where the distribution in mind is the ground truth
labels as a random variable whose set of possible labels at each increment
grows. If training, validation, and testing splits are not predefined, each
resulting increment from above may be split using any of the typical
methodologies, such as 8:2 train-to-test ratios, or even multi-fold cross
validation. If they are pre-defined, then validation and testing splits are
separated into increments based on their classes corresponding to the classes
in the training split, ensuring the validation and testing splits contain the
training classes at each increment.

Figure 3:  A visual representation of KOWL-718’s class density per increment
as used in the experiments when the OWL protocol is applied to Kinetics-400 to
Kinetics-700. The incremental learning uses a unified mapping of the Kinetics
activity classes based on the most recent label first. Each increment contains
a new set of unknown activities and the prior known activities persist as long
as the original Kinetics datasets contained samples for them. The most
frequent novel classes were introduced first within their respective dataset
to help balance the samples over increments. See Section 4.

Increment | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10
---|---|---|---|---|---|---|---|---|---|---|---
Classes |  |  |  |  |  |  |  |  |  |  |
Known | 409 | 409 | 455 | 501 | 547 | 593 | 636 | 653 | 670 | 687 | 704
Novel | 0 | 46 | 46 | 46 | 46 | 43 | 17 | 17 | 17 | 17 | 14
Train Samples |  |  |  |  |  |  |  |  |  |  |
Known | 218371 | 16082 | 19148 | 24661 | 32391 | 44687 | 34287 | 36112 | 38527 | 41855 | 47128
Novel | 0 | 3163 | 5513 | 7730 | 12298 | 25538 | 1870 | 2415 | 3331 | 5273 | 10144
Test* Samples |  |  |  |  |  |  |  |  |  |  |
Known | 35256 | 1128 | 307 | 491 | 732 | 1096 | 4892 | 4990 | 5197 | 5473 | 5883
Novel | 0 | 179 | 232 | 266 | 372 | 612 | 163 | 208 | 277 | 411 | 684

Table 1:  KOWL-718’s resulting number of known and novel classes and their
samples per increment using the configuration in Section 5: Most recent label
first, first come first assigned data split, and most frequent novel class
first per future dataset. The initial increment 0 uses the same splits as
Kinetics-400 with 18,016 validation samples not listed above. The test split
consists of Kinetics-600’s validation and test splits for the increments in
range [1, 5] and the Kinetics-700-2020 validation split for increments in
range [6, 10] given the Kinetics-700-2020 test set labels are not yet publicly
released. Totaling 630,524 training samples and 68,849 test samples overall.
*See Appendix C.1 for further breakdown and a note on link rot where videos
are lost.

This protocol was used to construct a Kinetics Open World Learning experiment
with 718 classes (KOWL-718) that followed the chronological release of the
original datasets. By following the chronological release of the original
datasets, the practical gathering of data and changing of a dataset over time
is tracked. KOWL-718 starts with Kinetics-400 (?) as the initial increment,
followed in order by Kinetics-600 (?) split into 5 increments, and then
Kinetics-700-2020 (?, ?) split into 5 increments, as seen in Figure 3. The
human activity labels between the original Kinetics datasets differ as more
were added and existing classes were either removed, renamed, or reorganized.
Some classes were separated into two or more classes, or samples that were in
one class were moved to another. Given this inconsistency between labels, we
constructed a Kinetics unified label set that prioritizes the most recent
changes made to the datasets first: following Kinetics-700, then Kinetics-600,
then Kinetics-400. This means that the samples that were in prior versions and
are still in Kinetics-700 have a Kinetics-700 class, while the samples removed
from future versions keep their most recent class. The resulting most-recent-
first label set totals 718 activities. If following this order with consistent
labeling, this ordered release of samples allows for predictors pretrained on
the different original datasets to be used as checkpoints to start from for
future predictors to skip prior increments, if so desired. The unified
Kinetics criteria are as follows:

  * •

Label assignment: Most recent label first. Thus, using Kinetics-700-2020
labels for all samples when available. If the samples in Kinetics-600 are not
used in Kinetics-700-2020, then their activities as assigned from Kinetics-600
are used. Same applies for samples in Kinetics-400 that are not used in either
Kinetics-600 or -700-2020. Under this configuration, such activities are not
represented in later datasets, which matches the actual release of the data.

  * •

Data split assignment: First come, first assigned. This follows how the data
were released throughout the years, keeping persistent samples within their
first assigned data split, and avoiding the issue of training samples from
earlier Kinetics datasets being used in validation or test sets of later
Kinetics datasets.

  * •

Introduction of novel activities: Most frequent first. When adding a dataset
as released in time, this means novel activities within Kinetics-600 and
Kinetics-700-2020, the most frequent novel activity is introduced first such
that there are more samples of that class across the $N=5$ increments. This
aids in balancing out the number of samples across the remaining increments of
a dataset.

While using the most recent labels means those re-labeled samples from older
datasets are more accurate, it does result in class imbalance in the prior
Kinetics datasets. As such, when using the OWL protocol to partition
Kinetics-600 and -700 samples whose labels were fewer than the remaining
increments, they were simply included only within the increment they were
introduced in. This occurs specifically within the validation splits of these
increments due to their smaller size relative to the training and testing
splits. Given this we combined the validation and test splits of Kinetics-600
into our test set at those increments as seen in Figure 3 and Table 1. Due to
using the most recent label first scheme, the Kinetics-400 and -600 training
splits have classes that are not contained within their validation or testing
sets throughout the incremental learning until the final increment. The
resulting experimental dataset includes a starting increment consisting
entirely of Kinetics-400 with a total of 10 following increments each with
training and test splits determined by the original Kinetics-600 and -700
validation and test data using the most recent label first for each video. The
Kinetics-700 validation data serves as the test data in KOWL-718 given the
original test labels are not publicly released. Class balance is further
detailed and visualized as a bar chart of occurrences in the Supplemental
Material. An ablation study using annotations from Kinetics-AVA (?) of the
predictors based on certain video conditions such as the number of people
within a key frame of a video is included in Section 5.3.

###  4.1 Evaluation

Using an incremental OWL HAR dataset, incremental learning starts with fitting
on the training data in the initial increment that includes the known classes
in $K$, which matches typical supervised learning classification. As seen in
Figure 2, the next increment’s data is introduced and the predictor is
evaluated on all of that data prior to any feedback being provided, such as
the ground truth labels. Novelty detection and recognition of human activities
may now be evaluated. Recall that novelty detection is detecting when a sample
belongs to an activity class unknown to the predictor at that time, and recall
that novelty recognition is the classification of those detected unknown
samples into potential class-clusters based on their feature representation
and relation to known classes within the feature representation space. The
prediction output may be a single nominal class or the probability vector of
estimated likelihoods of the mutually exclusive classes. With the pre-feedback
class predictions, the evaluator may measure the performance of the predictor
on HAR classification, novelty detection, and novelty recognition. The
confusion matrix captures all the information from a sampling of the marginal
and joint distributions between the predicted most likely classes and the
actual labels, which enables deriving measures often useful in evaluating
classification and clustering. These include accuracy, Matthews Correlation
Coefficient (MCC), and Normalized Mutual Information (NMI). Accuracy is easily
human interpretable and assesses the correctness of the symbolic mapping of
class labels, while MCC assess the linear correlations between the predictions
and the ground truth labels, and NMI assesses both the linear and non-linear
correlations (?). The types (bold) of evaluation measures necessary to make an
informed assessment of the predictors are as follows along with our chosen
measure (italics):

  1. 1.

Exact Symbol Matching: Accuracy: The primary task, especially in
classification, tends to desire the predictor to output agreed upon labels
that are deemed useful to understand what the class pertains to. For example,
we want unobfuscated labels of the activity being performed, such as “walking
the dog” instead of “class_379”. Accuracy is a common measure that evaluates
how many samples have this correct label.

  2. 2.

Linear Correlation: Matthews Correlation Coefficient (MCC): While easily human
interpretable, at times the accuracy may measure larger values for ill-
performing predictors, such as when a predictor outputs the most frequent
label in an imbalanced label set. Linear correlation measures such as
Pearson’s r coefficient or the equivalent Matthews Correlation Coefficient
(MCC) will express low values for such undesired behavior. These values range
from $[-1,1]$, where -1 is inversely or negatively correlated, 0 is no
correlation, and 1 is positively correlated.

  3. 3.

Task-Relevant Information: Arithmetic Mean of Entropies Normalized Mutual
Information: To further separate from the dependency on what is the exact
symbol, non-linear correlations may be measured as well. Such measures place
the importance on the information learned, which includes both linear and non-
linear relationships, such as the mutual information between the predictions
and the actual labels. While mutual information is the measure to use to
determine how much of the task-relevant information was learned (?),
practically, the mutual information is unbounded and requires enough samples
to properly estimate the actual information of the task and predictions. With
this said, normalized variants of mutual information are available, such as
the information quality ratio or the normalization by the arithmetic mean of
the entropies, where the former is a more exact normalization when enough
samples are available, and the latter is a better estimate when not enough
samples are available, albeit not as interpretable in its reported ratio of
information learned. When the mutual information is normalized, it may be
reported within the ranges $[0,1]$, where 1 indicates all task-relevant
information was learned.

To observe only one of these measures ignores important performance
information in the others. A well-performing predictor will maximize all of
these point-estimate measures in- and out-of-sample.

Figure 4:  The raw confusion matrix and its reductions to assess performance
of a predictor’s chosen activity classes at a time-step. The raw confusion
matrix compares the actual known and unknown classes at a time-step to the
predictor’s chosen activity class labels, including any recognized unknown
classes. Given a predictor will never know the symbolic mapping of the actual
unknowns at the current time-step, the confusion matrix will have sub-matrices
of zero occurrences as indicated above. The reduction of known or unknown
classes is performed to focus on the performance on subsets of classes or
supersets in the case of detection. The confusion matrix captures all the
information from a sampling of the marginal and joint distributions between
the predictions and the actual labels, which enables deriving measures often
useful to evaluate classification. See Section 4.1.

Open world recognition, which is inherently a classification task, may be
evaluated at each time step using a confusion matrix and any of its derived
measures, such as those mentioned above. Depending on how classes are treated,
one may observe the “raw” confusion matrix where the predicted labels and
unknown ground truth labels remain as they were received. In this work we
execute three different reductions of the confusion matrix’s labels to assess
three specific subtasks listed below for open world recognition. These
reductions are depicted in Figure 4. In addition to evaluating these three
subtasks, a fourth subtask is necessary to assess the time-series information
of reacting to novelty as it occurs through the incremental learning. In
detail, the four subtasks are:

  1. 1.

Classification Task: The primary activity classes of interest along with a
single unknown class to account for any novel or unknown classes. To evaluate
this, reduce the predictor’s recognized unknown classes into the general
“unknown” class, indicating when the sample is deemed something other than
that which is known and deemed important to the task. This is the typical
assessment used in the original Kinetics protocol, minus the catch-all
“unknown” class.

  2. 2.

Novelty Detection: the binary classification of known vs. unknown. All known
classes are reduced into a single “known” class, and all unknown classes are
reduced into a single “unknown” class.

  3. 3.

Novelty Recognition: the unsupervised class-clustering separate from the
classification of the known classes. To focus on evaluating this, reduce all
known classes into a single “known” class. The measures derived from the
resulting confusion matrix must account for the class labels not using the
same symbols, even though they are the same cluster. This includes measures
such as mutual information, as used in this work.

  4. 4.

Novelty Reaction Time: The time it takes to react to novelty, which in the
case of HAR is the reaction time, measured in samples, to detect novel
activities after a single sample has occurred. Here, as in novelty detection,
the known classes are reduced and the unknown classes are reduced, resulting
in a binary classification assessment. The difference here is that the novelty
reaction time measures how many samples pass after the occurrence of a novel
activity’s sample.

Figure 5:  A visual depiction of the variables involved in the novelty
reaction time measurement used in this work. An increment consists of samples
that belong to intermixed known and novel classes. $a$ is the sample index of
the first instance of novelty in the increment. $z$ is the index of a final
instance of novelty in the increment. $d$ is the index of the sample that the
predictor detects as the first occurrence of novelty. $m$ is the number of
novel samples within the inclusive range $[a,d]$. $r$ is the total number of
novel samples within the increment. These are used in the harmonic mean in
Equation 1.

We use the following procedure to measure novelty reaction time within a
single increment whose variables are visualized in Figure 5: When the first
novel sample occurs at the sample index $a$, record the number of novel
samples $m$ observed before the first sample index of novelty $d$ is detected
by the predictor at or after $a$. Let $z$ be the final sample index of the
increment and $r$ be the total count of novel samples within the increment.
The following harmonic mean is used to measure the novelty reaction time:

$(\frac{(\frac{d-a}{z+1-a})^{-1}+(\frac{m}{r})^{-1}}{2})^{-1}=\frac{2}{\frac{z+1-a}{d-a}+\frac{r}{m}}$
(1)

where $\frac{d-a}{z+1-a}$ is the fraction of samples that pass before novelty
is first detected and $\frac{m}{r}$ is the fraction of total novel samples
that occur before detection occurs. $z+1$ is used because it reflects the
total number of samples with a zero index. If novelty is never detected, then
that corresponding fraction would be 1, otherwise it approaches 1 in the limit
of $z$ to infinity. Note that in the demonstration of KOWL-718 below, all
samples in the increment are given to the predictor at once by the evaluator,
rather than in “sub-increments” or batches.

All of these measurements require a clear distinction between what is known
and what is novel. In the majority of this paper’s experimental demonstration
in Section 5, the known classes are based on what activity class labels the
predictor knows within its experience at that moment in time. All of their
samples are thus deemed known or novel to the predictor based on this alone.
This may be referred to as novel-to-predictor, or as predictor novelty. This
is different from the novelty determined by if a predictor ever has
experienced a sample from a certain novel class, which only the evaluator
could ever know without feedback to the predictor. This may be referred to as
novel-to-evaluator, or as “actual novelty” as defined by (?). The evaluator
may record the measurements from the above four subtasks above in either way
as long as the reduction of knowns and unknowns follows the appropriate method
of determining novelty for that case.

###  4.2 Requesting Feedback

After evaluating the predictor on the new increment’s data, feedback may be
offered to it. The supervised incremental learning paradigm (?, ?) releases
all of the labels (100%) to the predictor for it to be fit. The experiment may
also only release a subset of the labels, such as none (0% feedback) or
partial feedback, such as 50% of the samples for this new increment. Our OWL
protocol gives the predictor another subtask of requesting feedback for itself
from the evaluator after every pre-feedback evaluation. This feedback request
takes the form of an ordered list of the samples’ unique identifiers. The
evaluator then determines how much feedback will be returned and returns it
starting with the prioritized ordering requested by the predictor. This kind
of experimental design matches that of human-in-the-loop feedback in practice
where human labeling is costly and figuring out a subset of samples of
interest to be annotated is desirable. Another option for feedback types is
returning the performance measurements on the increment, which is simply a
controlled validation dataset where the actual labels are not released. The
amount of feedback tested in the experimental demonstration of KOWL-718 in
Section 4.1 includes: 0%, 50%, and 100% feedback ground truth labels of the
incremental training set.

###  4.3 Evaluating Tolerance to Nuisance Novelty

The dataset and above measures assess the learning of task-relevant
information, and how novel activities are handled over time. It may be of use,
as it is in practice, to assess the tolerance of the predictors to certain
variables they are desired to be invariant against, such as task-irrelevant
information or nuisance novelties. To do this in HAR, the videos may be
visually transformed in ways unseen in training and any corresponding change
in the above performance measures may be recorded. If they do not encounter a
significant change in performance, then they are deemed tolerant to such
visual transformations, such as perspective rotation, hue shifts, visual blur,
or noise. Otherwise, such transformations that dramatically affect performance
can indicate predictor model weaknesses and areas of improvement. This may be
assessed such that different novel visual transforms occur over time and thus
are part of learning over time. Otherwise, the predictor’s state may be saved
and then reloaded in isolation to evaluate how the predictor at that moment
would handle the encountered nuisance novelty. The experiment in Section 5.4
takes the latter approach, loading the saved checkpoints of post-feedback
internal model state for that increment and then continuing the incremental
learning until the next feedback is given using only the visually transformed
videos of the original increments’ samples.

Figure 6:  An OWL predictor template for HAR with novelty. Learning tasks in
an open world requires the subtasks of feature representation, task-specific
classification, and novelty recognition to not only detect, but also learn
novel classes over time. Feedback is essential to inform the predictor of when
to improve at subtasks. Upon request from the predictor, external feedback may
be provided from humans as in active learning or from other predictors. See
Section 4.4. The models used in our experiments have a frozen feature
representation (X3D or TimeSformer) pretrained on Kinetics-400 with a fine-
tuned classifier updating every increment when given human labels as feedback.
0%, 50%, and 100% human feedback is examined. More predictor details can be
found in Section 5.1.

###  4.4 A Template for OWL HAR Predictors

Given the above formalization, a template for OWL predictors that manage
novelty in a HAR domain may be defined to provide an outline of what such
algorithms must include to perform well in practice. Figure 6 shows a template
that includes the subtasks of feature representation learning, task-specific
classification, and novelty recognition. OWL is inherently a task performed
over time. Thus the predictors must account for this and should adapt to
novelties that affect task performance (?). These predictors may be able to
request external feedback either from human experts as in active learning or
from other predictors that are accessible to them.

The first subtask of all OWL predictors for HAR is feature representation
learning, which is the learning of the mapping of the input video sample to an
informative encoding. As mentioned in Section 2, feature representation plays
a key role in not only learning HAR but also detecting and recognizing novel
activities.

The second subtask is the classification of known human activities as defined
by the task given to this predictor, which means there is a subset of human
activities that are task-relevant and use a specific semantic class labeling
system. Class labels may differ between datasets and the specific HAR task.
The original Kinetics dataset’s evolution from 400 to 700 classes is an
example of not only more classes being available over time that are deemed
relevant, but also a change in the actual class structure. For example, some
samples are labeled as “brushing hair” in Kinetics-400 were given the new
label “combing hair” in Kinetics-700 to differentiate between using a brush
versus a comb.

The third substask is hadling novelty, which is referred to in Figure 6 as
novelty recognition. Novelty recognition includes novelty detection, where
being able to detect novelty facilitates the incremental learning of new
information, as it is collected over time. In Figure 6, these subtasks are
ordered by sequence of typical performance, especially when focused on novel
activities. However, novelty recognition can be applied to either the feature
encodings from the feature representation space or the task encodings from the
task representation space. Performing novelty detection and OWL in the feature
representation space is beneficial because it provides more information about
the input, however this includes task-irrelevant information and as such
nuissance novelties may trigger irrelevant novelty detections and possibly
affect novelty recognition performance. When the feature representation
learning begins to approach closer to the classification task, task-irrelevant
information is naturally lost where task-relevancy is determined by the $K$
known classes. Performing novelty detection or recognition within a feature
representation space that is closer to the task may result in ignoring novel
information, including future task-relevant information.

As noted in Figure 6, these three subtasks may be completed separately by
different processing modules or together by one or two modules. The important
underlying design is that these subtasks share information with one another
and tend to be performed sequentially as ordered here, especially when the
novelty of interest is in the class activities.

As part of updating the predictor’s internal state, external feedback may be
leveraged, when possible, that is either provided from human annotators as in
active learning, or from other predictors. Feedback from human annotators
could be labels of the detected unknown classes, labels of notably incorrect
samples in the known classes, or some other signal of performance informed by
human annotators. Feedback from predictors can be similar or even more
nondescript signals of feedback. To properly use this external feedback is a
subtask in itself as sometimes the feedback may be in a form that does not
directly relate to internal mappings of classes or may even contain noise. As
mentioned in Section 4.2, the predictor may request an ordered prioritization
of the samples for feedback. The partial feedback of input samples results in
semi-supervised updating of the predictor’s internal state.

To enable informed comparison of predictors on a KOWL experiment, we relate
the six properties of incremental learning algorithms from (?) to the
terminology we’ve used throughout this paper. If we have a prior introduced
term for it, then their term is in parentheses. Otherwise, we use the original
names and quote their definitions. We also add a seventh property, “prior
knowledge,” as it relates to feature representation learning and is
fundamentally an important part to differentiate types of predictors on this
benchmark.

  1. 1.

Complexity: “[The] capacity to integrate new information with a minimal change
in terms of the model structure.” This refers to the internal state of the
predictor, specifically what that state is and how it changes, e.g., the
predictor’s number of parameters and changes in their values.

  2. 2.

Experience (Memory): The prior mentioned predictor experience may be limited
in memory space. When this is the case, performing OWL for HAR may become more
difficult and nuanced as the predictor needs to handle maintaining the key
information of the HAR classes and any information of use for differentiating
novel from known activities classes.

  3. 3.

Regret (Accuracy): The predictor’s internal assessment of its own performance.
This is a separate and possibly similar or entirely different calculation than
what the evaluator uses to assess the predictor’s performance.

  4. 4.

Timeliness: “[The] delay needed between the occurrence of new data and their
integration in the incremental models.” In the case of the KOWL protocol, the
time for the predictor to perform its operations, such as updating its state
and performing inference.

  5. 5.

Plasticity: “[The] capacity to deal with new classes that are significantly
different from the ones that were learned in the past.” In the case of KOWL
experiments, the predictor may require feedback or may be able to learn in a
semi-supervised manner.

  6. 6.

Scalability: “[The] aptitude to learn a large number of classes, typically up
to tens of thousands, and ensure usability in complex real-world
applications.” KOWL-718 has 718 activity classes and the rate of novel classes
per increment is rather consistent as seen in Table 1.

  7. 7.

Prior Knowledge: A predictor’s prior knowledge must be explicitly stated to
know how to properly compare it to other predictors. There are predictors with
no prior knowledge, i.e., those without any prior training on data external to
this benchmark or without Bayesian priors selected through information about
how the predictor performed on prior runs of KOWL experiments with the same
data. Before the predictor has seen the first sample in the initial step’s
training split, the predictor should essentially know nothing, otherwise known
as being maximally uncertain (?). This is favorable to the scientific method
as it preserves the closed system of controlled evaluation of a predictor’s
learning on this benchmark data under this protocol.

A predictor with prior knowledge may still be evaluated on a KOWL experiment,
but we strongly recommend comparing predictors with prior knowledge against
those with similar prior knowledge. For example, if there are pre-trainings
being used, either use the same pre-training feature representations or be
trained on the same external data before starting a KOWL experiment.
Otherwise, if the point is still to assess the predictor’s learning of the HAR
task within the KOWL experiment, special care needs taken to demonstrate that
the prior knowledge is irrelevant to learning the HAR task within the KOWL
experiment. If one only cares if their predictor can perform well on the
benchmark regardless of informative priors, for example to answer the question
of does the prior share information with the benchmark’s task, then of course
they are free to do so, but it must be acknowledged that this mode is not
assessing the predictor’s learning of the task as represented by the KOWL
experiment’s data alone.

All baseline predictors examined in the below experiments have their
experience as an unlimited memory buffer in the demonstration examples. This
can be treated as an experimental parameter changed when analyzing different
predictors on KOWL-718.

##  5 Experimental Demonstration

The following experiments demonstrate the OWL protocol as applied to the
Kinetics dataset series to form the KOWL-718 benchmark. Multiple feedback
amounts were examined, consisting of 0%, 50%, and 100% feedback as ground
truth labels of the incremental training split. The predictors determined the
ordering of samples for which to receive feedback. Alongside the standard
evaluation as specified in the above protocol, which specifically assesses the
incremental open world learning of human activities, we conducted an ablation
study of the predictors’ performances given Kinetics-AVA annotations. We also
examined the state-of-the-art feature representation tolerance to unseen
visual transforms to the Kinetics-400 videos.

###  5.1 HAR Predictors for Evaluation

There were two feature representation models explored in this work: X3D (?)
and TimeSformer (?). Both are models with promising performance on the HAR
task, and the Kinetics datasets specifically. X3D is a 3D ResNet (?) and
TimeSformer is a video transformer (?). Specific implementation details for
these models can be found in Appendix C.2.1. Both models are initialized at
their Kinetics-400 pretraining state and the layer just before the softmax
readout is used as the feature representation encoding. Starting with
Kinetics-400 pretrainings ensures no information about the future increments
is leaked, preserving a controlled incremental learning experiment. We note
that X3D and TimeSformer pretrained on Kinetics-400 were trained in a
supervised fashion meaning that their feature representation more closely
approaches the classification task, containing more task-relevant information
as defined by the known classes, but may lack future task-relevant information
in the videos yet to be deemed task-relevant at the initial increment.

The fine-tuned classifier that serves as our demonstrative baselines for this
OWL HAR benchmark is a single fully connected layer to the feature
representation that then outputs to the final softmax classification layer,
denoted as “ANN”. The fine-tuned classifier is the only state of the predictor
updated during the incremental learning. The ANN classifier always has an
unknown class in its softmax classifier and as novel classes become known, the
classifier’s output increases in size. Novelty detection is handled using a
threshold over the softmax output of the classifier where the initial
increment’s validation partition is used to set that threshold with an
accepted level of error, which was 10% in these experiments. The accepted
level of error represents a prior expectation of how well the validation data
is believed to represent the known classes, and was used only to ensure some
novelty detection occurred for demonstration purposes. The ANNs have no method
of handling novelty recognition, only detection.

Given the ANNs cannot handle novelty recognition, a semi-supervised classifier
was used to depict novelty recognition by learning the potential unknown
classes. This classifier, denoted “GMM FINCH”, consisted of a Gaussian Mixture
Model (GMM) on the feature representation space where the Gaussian components
were determined by the FINCH (?) clustering algorithm performed on each
class’s sample sets. If the known labels are available, then those samples
form different groups of points by class in which FINCH calculated the
clusters within each separately. In the hope of better learning the feature
representation space’s density for finding unknown classes, a Gaussian
distribution was fit over a class’s clusters to form the class’s GMM. Each
Gaussian distribution was fit using the uniformly minimum-variance unbiased
estimators for its cluster, which is simply the sample mean and covariance in
feature representation space. The samples that were deemed unknown served as
their own unknown class, which was fit with a GMM in a similar fashion. The
unknown samples were based on the predictions of the classifier after being
fit after thresholding on the logarithmic probabilities. The threshold was
found in a similar fashion to the ANNs using the validation data as all knowns
and a hyperparameter to establish an accepted amount of error on knowns in
hope of enabling better novelty detection in the future. A sample is deemed
novel to the known classes by this threshold if the sample has a logarithmic
probability less than the minimum maximum-likely class logarithmic probability
minus the likelihood adjustment found from the validation data given the
accepted error.

The use of the Extreme Value Machine (EVM) (?), which is an open world-
specific classifier, was also explored. However, its performance was found to
be worse than the ANN on the initial increment and in addition to its memory
constraints and runtime issues, it was left out of further incremental
learning analysis. The EVM in itself is only a novelty detector and cannot
perform novelty recognition on its own. See Appendix C.2.2 for details.

The properties of the predictor baselines used to demonstrate the experimental
process using the KOWL-718 benchmark are:

  1. 1.

Complexity: All baseline predictors used fixed feature representations of
either X3D or TimeSformer on Kinetics-400. Finetuned ANNs only adjusted their
weights when given feedback and they involve only one hidden layer. GMM FINCH
was updated in a semi-supervised fashion when given unseen data samples, with
or without evaluator feedback.

  2. 2.

Experience: All predictors examined have an unlimited memory buffer in the
demonstration examples. However, this could be an experimental parameter
changed when analyzing different predictors on KOWL-718.

  3. 3.

Regret: The Kinetics-400 pre-trained feature representations used the losses
respective to their model, X3D or TimeSformer. Cross-entropy was used to train
the finetuned ANNs. GMM FINCH used the logarithmic probability density
function for each class’s GMM.

  4. 4.

Timeliness: No time-limits were placed on a predictor’s inference or fitting
operations.

  5. 5.

Plasticity: The baseline finetuned ANNs can only detect unknowns. They do not
support recognizing unknown classes and must be given feedback labels to
account for new activity classes over time. The GMM FINCH predictors learn a
GMM in the fixed feature space per activity class and as such may identify
unknown classes without feedback as Gaussian distributions within the feature
space. The GMM FINCH predictors thus update whenever any new data is presented
to it, even without feedback.

  6. 6.

Scalability: All predictors were fit on 407 activity classes in the initial
increment and then incrementally encounter the remaining novel 311 classes,
totaling 718 classes. The rate of new classes per increment is seen in Table
1. No further assessment of scalability is demonstrated. See Appendix C.2 for
implementation details.

  7. 7.

Prior Knowledge: No external prior knowledge was used. Pre-trainings of X3D
and TimeSformer on Kinetics-400 were used only to expedite the experimental
process by skipping their fitting on the initial increment in KOWL-718, which
is the same input data.

Raw HAR Non-Cumulative Performance on KOWL-718

Fig. 7.1  Training Set

Fig. 7.2  Testing Set

Figure 7:  The non-cumulative incremental HAR raw confusion matrix performance
of the baseline predictors where the incremental evaluation for pre-feedback
is on whole numbers and post-feedback is on half steps. All pre-feedback
samples are out-of-sample, regardless of whether they are in the future
training data. The raw confusion matrix compares the top-1 predictions to the
target label without any reductions of knowns or unknowns. The predictors were
fit on all prior and current experienced data at each increment, except that
the ANNs did not use prior seen validation / test sets as unlabeled data as
the GMM FINCH recognizer did.

Task performance for Kinetics is often measured using classification accuracy
of top-1, top-5, or $1-\frac{\text{top-1}+\text{top-5}}{2}$. Given that our
unified labeling is inherently imbalanced, we examine not only accuracy, but
also Matthews Correlation Coefficient (MCC) and the normalized mutual
information (NMI) normalized by the arithmetic mean of entropies as they are
measures that better capture performance. They are still affected by class
imbalance, but will not have high values for any ill performing cases, such as
nearly always predicting the most frequent class.

As mentioned earlier, three feedback amounts were used in the incremental
learning of this demonstration, which were 0%, 50%, and 100% feedback using
the ground truth labels. 100% human label feedback is the typical supervised
incremental learning paradigm where all ground truth labels are given to the
predictor after an initial performance assessment at each increment. 0%
feedback is when no feedback of any form is given to the predictor and its
performance is examined based how well it detects novel classes as unknown,
and, if the predictor learns unknown classes over time, how its class mappings
correlate to the unknown ground truth. The ANN predictor baselines with 0%
feedback do not update under the complete supervised learning paradigm.
However the GMM FINCH predictors continue to update in a semi-supervised
manner without any further feedback labels. 50% feedback is the most nuanced
as the predictor must perform the subtask of requesting feedback for the
increment’s sample ordered by some prioritization to better perform the task.
Given the small spread of raw performance difference between the 100% and 0%
feedback ANNs (see Figure 7), the performance figures depict only GMM FINCH
performing 50% feedback for the simplicity of demonstration and the visual
readability of the figures.

HAR Novel-to-Predictor Classification Task Non-Cumulative Performance on
KOWL-718

Fig. 8.1  Training Set

Fig. 8.2  Testing Set

Figure 8:  The non-cumulative incremental HAR classification task performance
of the baseline predictors where the incremental evaluation for pre-feedback
is on whole numbers and post-feedback is on half steps. All pre-feedback
samples are out-of-sample, regardless of whether they are in the future
training data. The predictors were fit on all prior and current experienced
data at each increment, except ANNs did not use prior seen validation / test
sets as unlabeled data as the GMM FINCH recognizer did. TimeSformer serves as
the best feature representation. The ANN with 0% feedback past the initial
Kinetics-400 training data performs slightly worse than its incremental
learning 100% feedback counterpart. GMM FINCH predictors are found to overfit
their data and perform worse than the ANNs. The Kinetics-700 test data is the
original dataset’s validation data as no labels for the test data were
publicly released. See Section 4 for data specifics. See Figure 7 for the raw
confusion matrix plots without any reduction of known or unknown classes.

###  5.2 Open World HAR: Novel Activities Over Time

The time-series line plots, such as the figures for the classification task in
Figure 8, and the raw unreduced performance in Figure 7, are all non-
cumulative point-estimate measurements. This means that every measurement
stands for the performance only on the new data for that increment where at
the whole step the new data serves to evaluate the generalization of the
predictor in the pre-feedback phase and then the half-step evaluates the
predictor after being given the feedback requested, if any. Analyzing the
performance in a non-cumulative manner indicates the effect that each
increment’s samples have at that moment in time to better compare the
performance on pre- and post- feedback. As such, jumps in performance from one
increment to another may occur due to the number of samples and changes in
balance of the classes by introducing the increment’s novel classes. The
figures for assessing how the predictors handle novelty, such as novelty
detection Figure 9, novelty recognition Figure 10, novelty reaction time
Figure 11, all only show the performance at the pre-feedback phase, as no
novelty occurs in the post-feedback phase given the video samples have already
been seen.

####  5.2.1 HAR Classification Task Performance

The HAR primary classification task performance of the incremental learning
predictors was assessed over time as novel classes were introduced in each
increment, as seen in Figure 8. Note that this includes the reduction of
actual unknown and recognized unknown classes into the “unknown” catch-all
class. The performance of all predictors degrades over the increments as
expected, given that the feature representation is frozen at Kinetics-400 and
thus does not contain information about the new classes added within
Kinetics-600 and -700. This decrease in performance happens even when 100%
feedback is given, indicating that the learned feature representations from
X3D and TimeSformer do not generalize to unseen activities given the
Kinetics-400 training data to the rest of Kinetics-600 and -700 samples.
Otherwise, the classifiers would be able to perform better on the accuracy and
MCC measurements in Figure 8. Overall, predictors using the TimeSformer as
their feature representation out performed those using X3D. This increase in
performance comes at the cost of longer training and inference times as noted
in Appendix C.2.1. Given that TimeSformer consistently out-performs X3D, the
plots include only the GMM FINCH predictors using the TimeSformer feature
representations for clearer visual readability.

The post-feedback NMI for nearly all of the models using 100% feedback are
higher than their relative pre-feedback evaluation measurements with the
exception of the NMI of GMM FINCH increment 5. The finetuned ANNs with 0%
feedback have the same post-feedback performance as their pre-feedback
performance because no updates are made to their internal state after the
initial increment. The GMM FINCH with 50% feedback also experiences an
increase in performance in the post-feedback phases, but in the training set
either not as significant as the 100% feedback and in the testing set not
always improving relative to the increment’s pre-feedback measurements.
Nonetheless, this supports the belief of the value of informative feedback for
learning a task.

Notably, from both Figure 8 and the raw performance in Figure 7, the GMM FINCH
model seems to overfit the training data, as indicated by the low measurements
during the pre-feedback phases (whole numbers) and high measurements during
post-feedback phases (half-steps) on the training set and the low measurements
on the both phases in the testing set. The 0% feedback GMM FINCH is an example
of when to observe MCC and mutual information because on both the training and
testing sets in Figure 8 the accuracy indicates high performance due to
predicting unknowns frequently. But doing so does not correlate linearly or
non-linearly with the actual labels, as captured by MCC and the normalized
mutual information.

The predictors exhibit an interesting phenomena where on post-feedback their
accuracy and MCC either remain the same or drop, while the opposite happens
for most of their NMI scores. This is due to the fact that at post-feedback
the novel classes are no longer reduced to unknown if feedback labels for that
class were given to the predictor. The raw measures for these predictors
mostly remain similar in performance for post-feedback compared to their pre-
feedback, especially the finetuned ANNs, as seen in Figure 7.

HAR Novel-to-Predictor Novelty Detection Non-Cumulative Performance on
KOWL-718

Fig. 9.1  Training Set

Fig. 9.2  Testing Set

Figure 9:  The novelty detection performance of the predictors over
incremental learning on KOWL-718. Both novelty detection and recognition are
assessed at the beginning of each increment during the pre-feedback phase, and
the results are on the combination of all new data in that increment’s
training or test set. The first observation of an increment’s novel samples is
in the pre-feedback phase only. All pre-feedback samples are out-of-sample,
regardless of whether they are in the future training data.

Given the reduction of unknowns, the predictors with different feedback
amounts have fewer known activities to learn and more samples that fall under
“unknown.” Due to this, such predictors may have higher performance
measurements as seen in the 0% feedback finetuned ANNs on accuracy and MCC.
However the ANN’s NMI indicates they perform worse than their 100% feedback
counterparts. Furthermore, referencing the raw confusion matrix’s measures in
Figure 7, these increases in accuracy and MCC vanish, indicating it is due to
the reduction of unknowns. Also, the GMM FINCH 50% feedback at times exceeds
its 100% feedback counterpart on the testing set.

####  5.2.2 HAR Novelty Detection Performance

Figure 9 depicts the novelty detection performance of the predictors during
incremental learning. This is the binary classification task when the known
classes to the predictor at each increment are reduced to “known” and the
actual unknowns and predictor’s recognized unknown classes are reduced
together to “unknown.” We note that the 100% feedback GMM FINCH’s accuracy is
nearly identical to that of the known class frequency shown in Table 1. This
means that the GMM FINCH 100% feedback is predicting “known” nearly all the
time, which is an ill performing novelty detector and is captured by the low
measurements of MCC and NMI. Based on the MCC and NMI measures, the baseline
predictors demonstrate that novelty detection over the incremental learning of
KOWL-718 is not an easy task, and also demonstrate how accuracy may be
misleading. The best novelty detectors in Figure 9 would be the finetuned ANNs
using the threshold determined from the initial increment’s validation set.
Although demonstrative, the baseline performance leaves much to be desired and
indicates room for improvement from future predictors.

####  5.2.3 HAR Novelty Recognition Performance

HAR Novel-to-Predictor Novelty Recognition Non-Cumulative Performance on
KOWL-718

Fig. 10.1  Training Set

Fig. 10.2  Testing Set

Figure 10:  The novelty recognition non-cumulative performance of the baseline
predictors. The first observation of an increment’s novel samples is in the
pre-feedback phase only. All pre-feedback samples are out-of-sample,
regardless of whether they are in future training data.

Figure 10 depicts the non-cumulative predictor performance as they
incrementally learn KOWL-718. Accuracy and MCC are not useful to visualize
given that they require the symbolic mappings to be exact mappings, where
novelty recognition is inherently a comparison of clustering along with a
single catch-all “known” class. At best, accuracy would simply be overly
optimistic when predicting samples as known the majority of the time given the
balance of knowns to unknowns in KOWL-718, which is representative of real
world scenarios. Given this and the amount of samples per increment, the
mutual information normalized by the arithmetic mean of entropies is measured
per increment to assess the shared task-relevant information between the
actual labels and the predictor’s recognized unknown classes. Perhaps as
hinted at by the prior novelty detection performance, the baselines do not
perform well on novelty recognition with the majority of the NMI measurements
for the predictors falling below $0.1$. The TimeSformer GMM FINCH with 0%
feedback notably outperforms all the others starting from increment 4 onwards,
but does not exceed a measurement of $0.4$ NMI. This demonstrates the
difficulty of the semi-supervised learning task of novelty recognition and we
are hopeful that future predictors will surpass these baselines as more
researchers take up this line of work.

####  5.2.4 HAR Novelty Reaction Time Performance

Figure 11 depicts the novelty reaction time of the predictors on KOWL-718 and
indicates that the ANN models with thresholding for unknown samples all react
at the same time regardless of their feature representation model. The ANN
models react the slowest on the training set as it is released at the pre-
feedback stage but join the GMM-FINCH 0% model for the first five increments
as the fastest reacting predictors to novelty. The GMM-FINCH with 50% feedback
reacts the slowest out of all of the models over all sets, with only the first
increment having a notably less than 1.0 reaction time score. The GMM-FINCH
with 100% feedback is the most sporadic in its reaction times as evident in
its scores prior to the sixth increment. The GMM-FINCH with 0% feedback
consistently has the fastest reaction time of all models. This may be due to
tending towards guessing “unknown” in favor of “known,” as the prior MCC and
NMI measures in Figure 9 were very low. But its detection accuracy was not too
low overall and it did perform the best out of all predictors on novelty
recognition, which indicates that out of all of the baseline predictors in
this demonstration, it may be a contender for the best model in handling the
novelty-related subtasks of HAR.

HAR Novel-to-Evaluator Novelty Reaction Time Non-Cumulative on KOWL-718

Fig. 11.1  Training Set

Fig. 11.2  Testing Set

Figure 11:  The per-increment novelty reaction time measure from Equation 1 of
the baseline predictors. Unlike the prior measures, the optimum direction here
is minimization towards zero. The first observation of an increment’s novel
samples is in the pre-feedback phase only. All pre-feedback samples are out-
of-sample, regardless of whether they are in future training data.
TimeSformer+GMM-FINCH-0% is the best performing on reaction time given its low
values, while all of the ANNs perform the worst on the training set.
TimeSformer+GMM-FINCH on 50% and 100% feedback has lower reaction times early
on in both sets, but joins the ANNs in maximum reaction time value by the 6th
increment when the Kinetics-700 dataset begins.

###  5.3 Ablation Study of Performance given AVA-Kinetics

The measurements so far only examine the performance of the different subtasks
given novel human activities occurring in an open world. To better analyze how
performance is affected by characteristics of the video samples, an ablation
study of the predictors’ classification task performance over KOWL-718 was
conducted using annotations from AVA-Kinetics (?) to examine any difference in
performance given certain characteristics of the videos. AVA-Kinetics
annotated different types of information for a single key frame on a subset of
videos in the Kinetics datasets. Using these annotations, we were able to
observe the performance of the predictors under different circumstances, such
as the different types of Kinetics actions, the different number of AVA
actions occurring within a key frame, the number of humans that were in a key
frame of a video, and the amount of pixel area occupied by humans using the
arithmetic mean area of their bounding boxes in the key frames. AVA-Kinetics
provided a grouping of the Kinetics actions into three types. The AVA actions
differ from the Kinetics datasets’ classes, which results in the possibility
of multiple classes occurring in a single frame. AVA-Kinetics records the AVA
actions as bounding boxes over the humans performing the action. This enables
counting the number of AVA actions and humans occurring in a single key frame.
The total area of humans within a video was calculated by taking the
arithmetic mean of the areas associated with the bounding boxes for the key
frame. Then those mean areas were binned into “small,” “medium,” and “large,”
to enable an easier comparison of the amounts of average area the humans
occupied within the frame. To get the bins, the mean areas were put into
ascending order split into thirds uniformly over the samples.

AVA-Kinetics Ablation Study on KOWL-718 Testing Set for

HAR Novel-to-Predictor Classification Task Cumulative Post-Feedback
Performance

Fig. 12.1
Kinetics Activity Type

Fig. 12.2
Number of AVA Actions

Fig. 12.3
Number of Humans

Fig. 12.4
Total Area of Humans

Figure 12:  We conducted an ablation study of the classification task
cumulative post-feedback performance on KOWL-718 under different annotation
types using each video’s annotated key frame in AVA-Kinetics (?). Cumulative
means that all of the confusion matrices associated with the time-steps for
the post-feedback phase were summed together, adding together the occurrences.
AVA-Kinetics only covers a subset of Kinetics videos. Any videos not annotated
fall under “N/A”. AVA activity types are: Person Movement (PM), Object
Manipulation (OM), and Person Interaction (PI). Total area of humans is the
mean of the bounding box areas normalized by the total area of the frame,
which was then binned into thirds as small (SM), average (AVG), and large
(LG). See Section 5.3 for details and discussion.

Figure 12 depicts the ablation study on cumulative post-feedback performance
of the predictors on the HAR novel-to-predictor classification task over
KOWL-718. The measures over the three Kinetics Activity Types indicates
minimal difference in performance but with Person Interaction activities
having slightly worse symbolic performance across all predictors as depicted
by accuracy and MCC. The clearest depiction of a difference in performance
across the ablation study is the Number of AVA Actions within a single key
frame in 12Fig. 12.2. Especially for Arithmetic NMI, the measured mutual
information across all predictors increases as the number of AVA Actions
within a key frame increases. There is a subtle increase in the accuracy and
MCC of the ANN predictors for five AVA actions versus less than four for AVA
actions. This could be due to more AVA actions within a key frame aiding a
predictor in classifying the Kinetics activity class, but further detailed
analysis and possibly more annotated data would be necessary to support this
finding. One possibility is that there are so few samples for the videos with
more AVA actions, that they become easier to predict as they are learned. For
example, group activities may be more easily determined if the predictors can
determine Number of Humans follows a similar, albeit weaker, trend to the
Number of AVA Actions, as seen by the measures in 12Fig. 12.3. Total Area of
Humans has a subtle decrease in the performance measures across all predictors
as the mean area of the humans in the key frame increases. This could indicate
that more visual environmental context may better inform the predictor of the
Kinetics activity class. However, it is important to be wary of this as it
could also indicate overfitting due to over-dependence on the environment
rather than the humans performing the action.

State-of-the-art Model Accuracy on Kinetics-400 Transformed with Nuisance
Novelty

Figure 13:  To evaluate tolerance against novel video representations, the HAR
accuracy of state-of-the-art models on visually transformed videos was
recorded. X3D and TimeSformer pretrained on Kinetics-400 given transformed
Kinetics-400 videos both experience a decrease in accuracy when encountering
unseen visual transformations. The TimeSformer performed the best in all
cases. This shows these models are not invariant to such visual
transformations.  Figure 14:  Examples of the visual transforms on
Kinetics-400 validation videos unseen by the predictors, along with their X3D
and TimeSformer original pre-trained classifier predicted labels marked as
correct or incorrect. While not exact matches, the incorrect predictions have
an understandable visual misinterpretation in these examples. The actual label
was included in the top-5 predictions for all except for the marching
example’s X3D on color jitter and TimeSformer on flip and rotation; the
playing paintball example’s X3D on flip and color invert; and the smoking
example’s X3D on the original, Gaussian blur, and color jitter. TimeSformer
always predicted “smoking” as its second most likely guess for the smoking
example.

###  5.4 HAR Predictor Tolerance to Nuisance Novelty

The above evaluation focused on action class specific novelty, however machine
learning algorithms are desired to be tolerant to noise, and nuisance novelty
is inherently never before seen noise patterns or other phenomena that are
task-irrelevant. We assessed the tolerance of pre-existing state-of-the-art
models, X3D and TimeSformer, to novel visual representations by examining the
performance of these models on Kinetics-400 versus Kinetics-400 with visual
transformations not seen during training. The videos are transformed using six
different types of visual transformations unseen during training, shown in
Figure 14. These transformations were applied to the videos with randomized
parameters for each transform. The visual transformations were performed such
that identical transformations were performed on each frame from the same
video, with the exception of noise, which was random for each frame. The MCC
of each predictor for each visual transform is depicted in Figure 13. We find
that the Kinetics-400 pretrained X3D and TimeSformer model performance is not
invariant to such novel representations, especially color inversion and the
rotation of videos. However, TimeSformer performs the best overall and is
minimally affected by random Gaussian blur of videos.

##  6 Conclusion

With the increased interest in learning tasks in the face of novelty (?, ?),
we formalized HAR in an open world and provided a reproducible and extendable
OWL protocol to evaluate predictors in learning novel classes incrementally
over time. Applying this protocol to the popular Kinetics datasets, we
constructed a practical benchmark, KOWL-718, that serves as an example of the
difficult real world problem of learning human activities from videos as new
data becomes available, where unseen actions may be encountered. This
benchmark serves to exemplify the nuance in performing HAR in an open world as
multiple subtasks are involved and intertwined together. The provided OWL
protocol allows for analysis of different aspects of learning HAR in an open
world, including varying levels of feedback. Further analysis of predictor
performance was recorded when the videos are visually transformed or when the
number of people between videos differs. The demonstrative baseline predictors
indicate that while the learning of these subtasks together is possible, it is
nontrivial to excel at all of the subtasks at the same time. This primes
future work on this benchmark which is unlike many others and ripe for further
research.

##  7 Acknowledgement

This research was sponsored in part by the National Science Foundation (NSF)
grant CAREER-1942151 and by the Defense Advanced Research Projects Agency
(DARPA) and the Army Research Office (ARO) under multiple contracts/agreements
including HR001120C0055, W911NF-20-2-0005, W911NF-20-2-0004, HQ0034-19-D-0001,
W911NF2020009. The views contained in this document are those of the authors
and should not be interpreted as representing the official policies, either
expressed or implied, of the DARPA or ARO, or the U.S. Government.

##  A The Three Primary Spaces of Novelty for HAR

The following discussion for HAR follows from the framework introduced in (?).
The World $\mathcal{W}$’s state space is the space of all human activities
$s_{\mathcal{W}}\in\mathcal{S}_{\mathcal{W}}$ and an oracle knows the maximum
likely activity class for each activity. A dataset’s ground-truth human
activity labels along with the paired video serve as samples from the oracle.
The samples from the world are used to learn the task and assess performance
based on feedback either directly or indirectly from an oracle. The true task
in the world is only ever approximated via sampling and it is the job of the
oracle and the experimental designers to determine if the predictor is
learning that actual task. This means that the world dissimilarity
$\mathcal{D}_{\mathcal{W}}$ and regret measures $\mathcal{R}_{W}$ are never
truly known, but are approximated via supervised learning when training the
predictor. HAR is framed as a discrete classification task where predictors
output their predicted nominal class label, and as such the confusion matrix
and its derived measures, e.g., accuracy, Matthews Correlation Coefficient
(MCC), or Mutual Information or its normalized variants may be used to assess
predictor performance at each time-step of learning as the world regret
measure $\mathcal{R}_{W}$ when inverted. If the order or magnitude of
weighting between mutually exclusive activity classes are desired, then the
desired output of the predictor is a probability vector assigning probability
to each of the known classes and unknown recognized class-clusters. To analyze
these, ordered top-$k$ confusion matrices may be used in order to obtain
measure such as the top-$k$ accuracy. The world experience
$\mathtt{E}_{\mathcal{W}}$ is simply the past sequence of states indexed by
the time-step $t$. The world state transition function
$\tau_{\mathcal{W}}(t,s_{\mathcal{W}})$ is the experimental process used to
determine the next sample video to give the predictor, based on the current
time-step and world state.

The observation space $\mathcal{O}$ is the encoding of world states after
being processed by sensors, resulting in the sensor states $s_{\mathcal{O}}\in
S_{\mathcal{O}}$. In the case of visual HAR, the sensors are cameras that
capture video footage of human activities, so the state space consists of the
video frame with its color space. The observation dissimilarity measure
$\mathcal{D}_{\mathcal{O}}$ tends to be Euclidean distance or the difference
of video pixel values between frames. However, this dissimilarity function is
very literal and captures any and all changes in pixels or frames. If the
observation space is instead designed to ignore more task-irrelevant changes,
then a different dissimilarity measure may be used. For example, in the case
of smart cameras that may represent the sensor input in some encoding learned
by an artificial neural network, the dissimilarity measure used can be the
inverse of cosine similarity. The standard for most annotated HAR datasets is
to not assess the quality or noise introduced by the video camera beyond
possibly noting that noise exists or classes are imbalanced. Fully simulated
environments lend themselves best to this kind of assessment given that it
requires multiple human annotations to assess real world samples for video
quality, noise, and the presence of other novelties.

Figure 15:  Set representation of novelty spaces adapted from (?). We use the
term “predictor” in-place of “agent”. See Section 3 and Appendix A.

The observation regret measure $\mathcal{R}_{\mathcal{O}}$ is the regret the
predictor could have given the available information in this space. This
differs from the world regret measure because it does not necessarily include
all of the information that the world space contains. This regret measure is
only applied in practice when the experimental designers consider and address
the difference in information between the world space and the observation
space. $\mathcal{R}_{\mathcal{O}}$ is not directly applied in this paper’s
experiments as it is determined by the sensors used to capture the videos and
would be expressed by meta-data or further annotations of the videos. The
observation experience $\mathtt{E}_{\mathcal{O}}$ is the time-step indexed
history of prior sensor states, which is the videos in HAR. In the case of
real world applications, the $\mathtt{E}_{\mathcal{O}}$ may have a limited
capacity to store the prior states, and so this may be defined as a buffer and
thus forgotten states will be deemed novel if encountered again. The oracle or
experimental designer may know the truth in such a case, and assess the
predictor with this knowledge. The observation state transition function
$\tau_{\mathcal{O}}(t,s_{\mathcal{W}})$ is the transformation of the given
world state through the sensors that form the observation space.

The predictor space $\mathcal{{\hat{Y}}}$ is the space of the predictor’s
model and states
$s_{\mathcal{{\hat{Y}}}}\in\mathcal{S}_{\mathcal{{\hat{Y}}}}$. The HAR
predictor makes decisions or predictions about the activity labels, but
depending on setup of experiments and available data, may request feedback.
The predictor’s dissimilarity measure $\mathcal{D}_{\mathcal{{\hat{Y}}}}$ may
be cosine similarity, Euclidean distance, or any measure that applies to the
predictor’s encoded space of the activity phenomena. The predictor’s regret
measure $\mathcal{R}_{\mathcal{{\hat{Y}}}}$ tends to be its objective
function, e.g., loss function, regret function, or a heuristic. The
predictor’s experience $\mathtt{E}_{\mathcal{{\hat{Y}}}}$ is often limited by
physical and practical constraints given it is a computer program, thus it may
be in practice a finite sized memory buffer. In this paper’s experiments using
Kinetics, we did not encounter physical limits of the memory buffer. The
predictor’s state transition function
$\tau_{\mathcal{{\hat{Y}}}}(t,s_{\mathcal{O}},s_{\mathcal{{\hat{Y}}}})$ takes
the given observation state and performs HAR predicting the human activity
class while also detecting novelty and optionally updating its state.

##  B Formalism: HAR Novelty Set Relations

With defined novelty spaces for HAR, we may further specify the types of
novelty encountered in this domain following (?). Given the three novelty
spaces, the presence of novelty across them forms set relationships as
visualized in Figure 15. The entire list of defined sets of novelty given the
three novelty spaces for HAR is as follows:

Unanimous Novelty: This is novelty that occurs in the world, is observable via
the sensors, and is detected by the predictor. This is the True Positive case
of novelty detection. For example, correctly detecting the first “blowdrying
hair” sample or “swimming with dolphins” sample in Figure 1.

Imperceptible Novelty: Novelty that occurs in the world but is unable to be
sensed by the sensors. In HAR, this can be wavelengths of light that are
unable to be sensed, such as infrared or ultra violet light that cannot be
recorded with an RGB sensor. This could also include novelty that is not
within the camera’s field of view or is occluded, which could affect the
activity being recorded. For example, in a multi-person team sport, if
something happens off camera that causes all teammates to stop and stare, then
the cause of them staring is a novelty imperceptible to the camera.

Ignored Observed Novelty: Observed novelty that is ignored by the predictor
because it is deemed as task-irrelevant or because the predictor is simply
unable to interpret the novelty in its input. The ironing hair example from
Figure 1 is a case where the predictor sees hair being brushed, but does not
recognize the hair iron given it is not task-relevant based on the prior known
classes and data.

Ignored Faux Observation Novelty: This is novelty that is introduced in the
observation novelty space that is ignored by the predictor and thus not deemed
as a novelty to it. An example is when the predictor is tolerant to hue shifts
in the visual input that occur due to sensor error and performs as expected on
the HAR task.

Faux Observed Novelty: In contract to Ignored Faux Observation Novelty, Faux
Observed Novelty is novelty that originates in the observation novelty space
but is also detected as novelty by the predictor and thus is included in the
predictor novelty space. Following the hue shift from the prior example, this
is when the predictor is not tolerant to hue shifts and detects them as
novelty.

Faux Agent Novelty: This is when the predictor falsely detects novelty in its
input space (feature space), otherwise known as a False Positive novelty
detection. In Figure 1, if the third video of “walking the dog” was classified
as a novel human activity class, this would be the agent misunderstanding the
visual input of three dogs on a leash as a new class. Perhaps seeing three
dogs together is novel, but in this case the human activity is still known and
the presence of more dogs results in a nuisance novelty.

Unverifiable Novelty: This is when a predictor correctly detects novelty in
the state, but this cannot be confirmed by the predictor because the sensors
cannot sense this novelty. This occurs when the predictor makes a lucky guess,
leverages latent knowledge about that task, or uses knowledge external to what
was found in the sensors, such as prior knowledge included by human designers.
If a camera’s point of view misses a novelty that has occurred off camera,
then at that moment of time it cannot verify that novelty occurred or exists,
but may be able to do so later in the future by simply shifting its point of
view.

##  C Additional Experiment Details

###  C.1 Data Specifics

Collecting the Kinetics Dataset is unfortunately not straight forward for such
a standard benchmark. The data is officially distributed by Google as a list
of YouTube IDs. This means that a large portion of it has fallen victim to
link rot where the links are broken and the videos are no longer available,
especially in the older Kinetics 400 and 600 sets. Fortunately, there are at
least two other sources for this data, a torrent for Kinetics-700222
https://academictorrents.com/details/49f203189fb69ae96fb40a6d0e129949e1dfec98
and Kinetics-400333
https://academictorrents.com/details/184d11318372f70018cf9a72ef867e2fb9ce1d26
, and a repository hosted by the
CVDF444https://github.com/cvdfoundation/kinetics-dataset. These two sources
offer a significantly more complete version of the dataset, and are convenient
ways to download the files. This paper used the repository hosted by the CVDF.
Information about the specific samples that were missing in our experiments is
available in the project’s source repository and supplemental data files.

See Table 2 for the breakdown of total number of samples used and activity
classes per increment, as well as their original source Kinetics dataset that
was used to construct KOWL-718.

Source Dataset | K-400 | Kinetics-600 | Kinetics-700-2020
---|---|---|---
Increments | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10
Train |  |  |  |  |  |  |  |  |  |  |
Known |  |  |  |  |  |  |  |  |  |  |
classes | 409 | 409 | 455 | 501 | 547 | 593 | 636 | 653 | 670 | 687 | 704
samples | 218371 | 16082 | 19148 | 24661 | 32391 | 44687 | 34287 | 36112 | 38527 | 41855 | 47128
Novel |  |  |  |  |  |  |  |  |  |  |
classes | 0 | 46 | 46 | 46 | 46 | 43 | 17 | 17 | 17 | 17 | 14
samples | 0 | 3163 | 5513 | 7730 | 12298 | 25538 | 1870 | 2415 | 3331 | 5273 | 10144
Validate |  |  |  |  |  |  |  |  |  |  |
Known |  |  |  |  |  |  |  |  |  |  |
classes | 409 | 409 | 455 | 501 | 547 | 593 | 636 | 653 | 670 | 687 | 704
samples | 18016 | 333 | 20 | 45 | 88 | 156 | 4892 | 4990 | 5197 | 5473 | 5883
Novel |  |  |  |  |  |  |  |  |  |  |
classes | 1 | 46 | 46 | 46 | 46 | 43 | 17 | 17 | 17 | 17 | 14
samples | 0 | 75 | 72 | 68 | 75 | 124 | 163 | 208 | 277 | 411 | 684
Test |  |  |  |  |  |  |  |  |  |  |
Known |  |  |  |  |  |  |  |  |  |  |
classes | 409 | 409 | 455 | 501 | 547 | 593 | 0 | 0 | 0 | 0 | 0
samples | 35256 | 795 | 287 | 446 | 644 | 940 | 0 | 0 | 0 | 0 | 0
Novel |  |  |  |  |  |  |  |  |  |  |
classes | 1 | 46 | 46 | 46 | 46 | 43 | 0 | 0 | 0 | 0 | 0
samples | 1 | 104 | 160 | 198 | 297 | 488 | 0 | 0 | 0 | 0 | 0

Table 2:  The resulting number of known and novel classes and their samples
per increment for the Kinetics-400, -600, and -700-2020 datasets using the
configuration in Section 5: most recent label first, first come first assigned
data split, and most frequent novel class first per future dataset. The
Kinetics datasets unfortunately experienced link rot and some original samples
have been lost. The single novel class and its single sample in Kinetics-400
test is still included in KOWL-718, but Table 1 excluded it for brevity.

###  C.2 Predictor Specifics

####  C.2.1 Feature Representation Model Details

##### X3D Details

The X3D (?) model used in this work uses a pure pytorch implementaiton of X3D
Multigrid (?), found at: https://github.com/kkahatapitiya/X3D-Multigrid. We
used the pretrained weights for the “M” model trained on Kinetics 400 in the
official distribution from FAIR found in (?). The reported performance of this
model is as follows: Top-1 10 view: 75.1, Top-1 30 view: 76.1. The
aforementioned repo’s model zoo from which we pulled the weights can be found
at https://github.com/facebookresearch/SlowFast/blob/main/MODEL_ZOO.md ported
into Kahatapitiya’s pure PyTorch implementation. After porting the weights, we
were able to approximately verify the model’s performance on our version of
Kinetics 400, with some variance due to issues such as dataset collection
difficulties and differences in video preprocessing, our performance is shown
in MCC as “Original” in Table 3

##### TimeSformer Details

The TimeSformer feature extractor used in this work was adapted from the
Kinetics-400 reference model distributed by (?) from
https://github.com/facebookresearch/TimeSformer. Specifically, we used the one
labeled as “TimeSformer” for which they gave the following statistics: number
of frames: 8, spatial crop: 224, top 1 accuracy: 77.9, top 5 accuracy: 93.2.
The code was only modified to return the feature embedding from the encoder as
an additional output. The raw TimeSformer result in MCC are shown in the
“Original” row of Table 3

####  C.2.2 Finetuned Classifier Model Details

The primary finetuned classifier models include an ANN that takes as its input
the feature representation of the samples and outputs a softmax classifier
output, which is the unknown class with the currently known classes together
as a probability vector. The ANNs used for both of the feature representation
models was a fully connected dense network of one hidden layer with a
LeakyReLu activation that then went to the softmax classsifier layer which was
sized as known classes + 1 for the unknown catch-all class. In experimentation
to find a suitable ANN architecture, multiple hidden layers and hidden layer
widths were examined, including multiple layers with skip connections through
concatenation. In the end, the single hidden layer model with the same size as
the feature representation vector was found to be the most performant, using a
dropout during training of 0.5 probability. We also explored different numbers
of total training epochs ranging from [1, 100]. We found that 100 epochs would
result in overfitting, as seen in Fig. 16, which compares the performance of
the Finetuned ANN for 100 epochs versus the original logit predictions of the
feature representation’s original classifier layer output. The runtimes of the
100 epochs ANN was approximately 8 hours of wall time on the Kinetics-400
dataset only using Kinetics-400 labels. The runtime of the single epoch ANN
was approximately 1 half-hour of wall time for the incremental learning
experiment at the cost of an estimated 0.05 MCC performance loss based on the
training run on only the Kinetics-400 data.

F. Repr. | Classifier | Train | Val. | Test
---|---|---|---|---
X3D | Original | 0.8701 | 0.7067 | 0.6893
X3D | ANN 100 | 0.9891 | 0.7054 | 0.6890
X3D | ANN 1 | 0.8414 | 0.6929 | 0.6740
X3D | ANN 1 + EVM | 0.5777 | 0.4836 | 0.4698
TSF | Original | 0.8988 | 0.7548 | 0.7361
TSF | ANN 100 | 0.9817 | 0.7546 | 0.7344
TSF | ANN 1 | 0.8566 | 0.7410 | 0.7237
TSF | EVM | 0.5552 | 0.4995 | 0.4938

Table 3:  The HAR classification task performance as measured by MCC on
Kinetics-400 with its original labels comparing different feature
representation and classifier combinations of the predictor. The Original uses
the X3D’s (?) and TimeSformer’s (?) (TSF) original classifier layers with
pretrained weights. These MCC values correspond to the bar chart in Fig. 16.

Figure 16:  The Extreme Value Machine (EVM) (?) was less performant with a
longer run-time to fit of about 8 hr. The ANN 100 was a fully connected ANN
with a single hidden layer trained for 100 epochs, which cost about the same
time as the EVM to fit. The ANN fit with only 1 epoch took dramatically less
run-time to fit and predict versus the rest at the cost of less than a
$\sim$2% decrease in validation and test performance to the original
classifier layer.

An Extreme Value Machine (EVM) was also evaluated. However, as seen in Fig.
16, the EVM performed worse than the ANN on the HAR task. Given this in
addition to its longer computation time, we excluded it from the experiment in
the main paper. Given that Kinetics-400 does is not necessarily representative
of Kinetics-600 and -700, ee note that the EVM may have resulted in better
novelty detection performance over the incremental learning than the base
finetuned ANN without any further novelty handling than a single unknown class
included at each time-step. The EVM’s runtime on the Kinetics-400 only
training totaled approximately 16 hours with a loss of performance relative to
both the feature representations’ original classifiers and the fine-tuned ANN.

Given time constraints and resource limitations, we only trained the finetuned
classifier ANN in the incremental learning with 1 epoch per increment. The ANN
trained with 1 epoch on Kinetics-400 was found to be somewhat less performant
than when trained with 100 epochs. The performance was still acceptable as a
baseline for the primary HAR task as seen in the main text for incremental
learning performance.

####  C.2.3 Hardware, Resources, and Computational Runtimes

These models were run on a variety of compute resources. In order to
facilitate experimentation, we first ran feature extraction on the entire
unified datasets (KOWL-718) with both the X3D and TimeSformer models. A full
run of the feature extraction takes approximately 30 hours for the X3D feature
extractor and approximately 23 days for the TimeSformer feature extractor when
using an Nvidia Quadro RTX 6000. In order to speed up the wall clock time of
this process, we broke the dataset into 60 equal sized batches which each took
about 30 minutes and 9 hours on the RTX 6000 respectively and ran the batches
in parallel.

###  C.3 Predictor Tolerance to Nuisance Novelty

In order to evaluate how the models held up to spurious representation
novelty, we evaluated model performance after the video was modified using a
number of common visual transformation techniques. This meant that we needed
to run a full pass on the unified dataset, KOWL-718, for each of the 6 types
of visual transformations. This resulted in approximately 194 GPU days for
feature extraction.

Once feature extraction was completed, the computational cost for the Kinetics
400 visual transform assessment became quite low in Section 4.3. During the
feature extraction process, the extracted features were saved to reduce the
overhead of running experiments multiple times. Without saving the state, the
compute overhead would have been substantially higher.

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