# Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis

Zhu Wang Sourav Medya Sathya N. Ravi
Department of Computer Science, University of Illinois at Chicago
<EMAIL_ADDRESS>

###### Abstract

Often, deep network models are purely inductive during training and while
performing inference on unseen data. Thus, when such models are used for
predictions, it is well known that they often fail to capture the semantic
information and implicit dependencies that exist among objects (or concepts)
on a population level. Moreover, it is still unclear how domain or prior modal
knowledge can be specified in a backpropagation friendly manner, especially in
large-scale and noisy settings. In this work, we propose an end-to-end vision
and language model incorporating explicit knowledge graphs. We also introduce
an interactive out-of-distribution (OOD) layer using implicit network
operator. The layer is used to filter noise that is brought by external
knowledge base. In practice, we apply our model on several vision and language
downstream tasks including visual question answering, visual reasoning, and
image-text retrieval on different datasets. Our experiments show that it is
possible to design models that perform similarly to state-of-art results but
with significantly fewer samples and training time.

##  1 Introduction

Recent work shows that utilizing implicit layers in deep network context can
be significantly beneficial for various Machine Learning (ML) tasks such as
hyperparameter optimization, meta learning, and solving inverse problems in
image processing settings [3, 14, 18]. In fact, if a desired input-output
requirement within a ML pipeline can be formulated as a convex optimization
problem, then we can use existing off-the-shelf CvxPy layers for guaranteed
subgradients [1]. For discrete requirements, we can simply use the convex
relaxation for backpropagation purposes. However, the existing implementations
may require more memory for additional slack variables or time to handle the
Jacobian, both of which are impractical in large-scale settings. More
recently, [10] argued that it may be possible to backpropagate through
implicit layers as long as we can write it as a Network Operator
$\mathcal{N}(\cdot)$ defined as a sequential application of an operator
$F(\cdot)$ that is guaranteed to converge to a (fixed) point in some
$T<\infty$ iterations. Formally, we consider network operators
$\mathcal{N}(x)$ that can be written as,

$\displaystyle\mathcal{N}(x;\Theta)=\underbrace{F\circ F\circ\cdots\circ
F}_{T\text{ times}}(x;\Theta),$ (1)

where $\Theta$ corresponds to parameters of a learning model such as the deep
network. Network Operators in (1) are convenient as it turns out that we can
backpropagate through such $\mathcal{N}$ with neither its full sequence (or
trajectory) nor solving an inverse problem with its Jacobian for gradients
[10]. Now, to see the utility of $\mathcal{N}(x;\Theta)$ in ML settings, we
consider the task of Trajectory prediction in Vision settings. Here, by using
$\mathcal{N}$ (with an appropriate $F$) as a clustering map on training data,
we can easily define performance improving loss functions. [42].

Figure 1: Model architecture of our proposed method. Inputs are an image and a
caption. Image is segmented to flatten patches, and the caption is parsed and
integrated with knowledge graph. Image-triplet pairs are fed into an OOD
detection layer. This layer detects noise concepts before passing features to
the cross-attention multimodal transformer encoder. Our pretrain objectives
contain ITM and MLM which represent image text matching, and masked language
modeling respectively.

In multimodal analysis, a model such as a deep network with parameters
$\Theta$ is trained to align multiple modalities such as visual and language
embedding spaces, [36, 33]. After learning $\Theta$, one can use the deep
network to predict the alignment of the modalities. For large-scale multimodal
tasks, [48, 20] provide guidelines for deep network architectures, and state
of the art strategies for fusing modalities.

In this paper, we consider the problem of designing Network Operator
$\mathcal{N}$ for incorporating external knowledge in large-scale Multimodal
pipelines. Intuitively, explicit knowledge corresponds to extracting the
relevant part of the idea while ignoring others which is often done by
considering the fidelity with multiple other modalities of the same input
data. Moreover, the explicit information brings the reasoning ability to the
multimodal analysis [15]. Indeed, there have been many solution schemes to
overcome technical challenges when features are from multiple modalities
simultaneously [6, 22]. However, these strategies are impractical when used
in-tandem with features derived from external knowledge graphs that are prone
to noise. For example, the fusion models in [11, 49] often fail to filter
noise pairs when pre-training on large-scale datasets, leading to slow
convergence. Specifically, there may be objects or concepts that are anomalies
in image-text pairs. For example, given an object “street” and relation
“locatedAt”, noisy (since absent in the input image) objects such as “fire
hydrant” are returned with high confidence by ConceptNet [40], undesirable for
training purposes.

Our key technical idea to handle noise from external knowledge as described
above is to score the features derived from KGs using an out-of-distribution
(OOD) detection layer within the pipeline. We show how such a layer can be
written as a network operator $\mathcal{N}$ in equation (1) for efficient
backpropagation. By deriving features corresponding to the relevant external
knowledge bases using a large pre-trained language model [24, 4], we show how
to accelerate training multimodal models seamlessly. To do so, we approximate
the density of in-distribution features using the concepts in the caption
corpus using recently developed out-of-distribution scores [31] for all
concepts. Thus, in this work, we provide an end-to-end training framework with
knowledge graphs and multimodal models in a differentiable manner, as
illustrated in Figure 1.

To showcase the effectiveness of our proposed OOD detection layer based on
$\mathcal{N}$, we perform several experiments using our pre-trained vision-
language models. To demonstrate the practical benefits of KGs in large-scale
multimodal pipelines, we perform extensive fine-tuning experiments on several
tasks such as visual question answering, and image text retrieval. We will now
briefly summarize our contributions here:

  * •

First, we show integrating explicit knowledge benefits multimodal fusion
models. Our pre-train model learns from visual and textual representations and
incorporate implicit and explicit knowledge seamlessly.

  * •

Second, our proposed OOD detection implicit layer can perform outlier
detection task with an efficient backpropagation manner in practical settings.

  * •

Third, our model outperforms six baselines on three downstream tasks on
several datasets. Moreover, we provide several ablation studies and an
interactive user study which shows that users is able to sustain their desired
in-distribution like inputs or features.

##  2 Related Work

Vision-and-language transformer. Most recent vision-and-language models have
shown promising performance in self-supervision learning as attention-like
mechanism for multimodal tasks. These models are pre-trained on large scale
image-text pairs and then finetuning on downstream VL tasks. UNITER [6], VinVL
[49], and PixelBERT [19] introduce the cross-modal architecture to learn joint
multimodal representations of visual and textual contents. ViLT [20], ALBEF
[22] and FLAVA [39] directly use image patch features extracting from backbone
and align with text features passing into a multimodal encoder. However, these
apply the strategies of scaling up the model by feeding more image-text pairs
or more layers. While a large model with more parameters can be effective, it
is inefficient for image and text retrieval tasks on large scale data and the
latent semantic information is still left to be explored.

Knowledge-based Multimodal analysis. In the area of vision and language
understanding, knowledge representation learning has been capitalized on image
and text or sentence matching [17, 12, 44]. These works aligned extracted
consensus or semantic concepts with visual concepts, but they applied
statistical method to construct concepts vocabulary. Recently, KRISP [27],
MAVEx [46], UnifER [16] and KAT [15] proposed knowledge based VQA to integrate
explicit knowledge. However, these mainly focus on VQA settings, the
effectiveness of incorporating external knowledge in vision-language pre-
training models still remains to be explored.

Out-of-distribution (OOD) detection. We discuss the out-of-distribution
detection methods in training deep neural networks. GEM [31] has shown a
provable OOD detection method to estimate the distributions. They demonstrate
the GEM score comparing with multiple prior OOD detection to provide the
theoretical and empirical guarantees. It is beneficial to use GEM score to
recognize irrelevant distributions of concepts during training. However,
previous works focus on the detection performance and it is unclear how to
utilize trustworthy OOD detection methods for improving pre-train model
performance in practice.

##  3 Knowledge-based OOD detection Multimodal Pipeline

To integrate explicit knowledge, we propose a novel architecture called VK-OOD
for multimodal analysis by fusing Vision and Knowledge features. By using an
Out Of Distribution (OOD) detector as the network operator $\mathcal{N}$, we
detect concepts that can potentially lead to slow convergence of upstream
and/or downstream layers during training. The model architecture is
constructed by vision and knowledge with texts end-to-end transformer encoders
and shown in Fig 1. VK-OOD aims at learning the joint representations of
images and texts integrating commonsense knowledge while filtering out noisy
concepts. Our method utilizes external background knowledge graph encoding
commonsense facts of the objects and relations about the objects in the input
images and captions. Our OOD network operator can can be adapted to various
downstream tasks.

Architecture. Given an image $I$ with a caption (question) $S$ in the form of
a sentence, our pipeline consists of the following steps, implemented as
differentiable modules in our VK-OOD architecture:

  1. 1.

Extract patches from Images, and transform to informative features using
standard vision models,

  2. 2.

Retrieve external knowledge triplets using Knowledge Graphs, and transform to
language features,

  3. 3.

While in the pipeline, we approximate density of in-distribution features to
filter each image-triplet pair (including ones derived from external
knowledge) into an OOD detection layer to filter outliers,

  4. 4.

Finally, we learn vision and language representations by a multi-modal cross-
attention transformer with multiple training objectives.

Obtaining Image Features Using Encoder Module.  We segment images into $P$
number of patches and extract $V$ as image features by vision encoder. We
denote the features extracted from the patches by $v_{m},m=1,...,P$.

Knowledge Triplets Retrieval Module.  We utilize semantic information from the
captions and derive triplets from external knowledge graph. We provide more
details of retrieval process in Appendix A. Then, similar to vision features,
we denote the final encoded features by $l_{ti},ti=1,....T^{cn}$.

###  3.1 Defining OOD detection layer as a Fixed Point Operator

Given Vision features $v_{m}$, and Language features $l_{ti},$ the goal of OOD
detectionn layer in our VK-OOD model is to compute a score
$s_{ood}^{ti}\in\mathbb{R}_{+}$ for each language feature $l_{ti}$. To do so
during training, we will now explain how to characterize the density or
distributions of the in-distribution (ID) triplet features for OOD purposes.

Finding Fixed Points for Forward Pass. Finite mixtures with $k$ components are
conceptually simple. Moreover, theoretically they can produce accurate
approximations to most density functions [30]. So, we approximate the density
of ID features using a Gaussian Mixture Model (GMM) and use
$\mu_{i}^{*},\Lambda_{i}^{*}=1,\dots,k$ to denote the (unknown) optimal means
and covariance matrices of $k$ components.

For GMMs, we solve for $\mu^{*}$ is by using the standard Expectation
Maximization (EM) algorithm [32]. Our main observation is that the update rule
used in an EM algorithm on the current iterate $\mu_{i}$ can be written as a
fixed point iteration as follows:

$\displaystyle\mu_{i}\leftarrow\frac{\sum_{ti=1}^{T^{cn}}\exp(-w(\mu^{ti}_{i}))l_{ti}}{\sum_{ti=1}^{T^{cn}}\exp(-w(\mu_{i}^{ti}))}$
(2)

where the weight of current iterate $\mu_{i}$ on $l_{ti}$ denoted by
$w(\mu_{i}^{ti}):=\sum_{i=1}^{k}\|\Lambda_{i}^{-0.5}(l_{ti}-\mu_{i})\|_{2}^{2}$.
We update $\Lambda_{i}$ similarly using
$(l_{ti}-\mu_{i})(l_{ti}-\mu_{i})^{T}\in\mathbb{R}^{d}$ in the numerator of
fixed point operation in Equation (2). This observation implies that we can
get an approximation of in-distribution density. Now we explain why EM
algorithm is our preferred choice from the theoretical standpoint to construct
the network operator $\mathcal{N}$ for OOD detection.

Benefits of EM algorithm. EM updates are provably convergent in various
settings, and it takes few iterations when it is guaranteed to converge [9].
This also implies that we can simply initialize $\mu_{i}$ randomly and perform
few more iterations. Moreover, since each iteration in GMM (2) is
differentiable, we can easily backpropagate through few iterations of EM
algorithm. However, since the denominator contains terms that are also unknown
parameters, such update schemes may be numerically unstable. In such cases, we
can simply use recently proposed gradient based EM algorithms as the network
operator, for fine tuning deep networks in large scale settings, see [37] for
convergence analysis.

Algorithm 1 Fixed Point Network Operator based Out-of-Distribution Detection
Layer for Language Features $l_{ti}$

Input

$l_{ti}\in\mathbb{R}^{d}~{}\forall~{}ti\in[T^{cn}]$$\triangleright$ Language
Features

$\mu_{i}\in\mathbb{R}^{d}~{}\forall~{}i\in[k]$$\triangleright$ Initial GMM
Means

$\Lambda^{0}_{i}\in\mathbb{R}^{d\times d}~{}\forall~{}i\in[k]$$\triangleright$
Initial GMM Covariances

$\lambda_{1}>0$ in Equation (4)$\triangleright$ OOD threshold parameter

$T\in\mathbb{N}$$\triangleright$ Maximum Iterations

Output

$G_{ti}\in\\{0,1\\}$$\triangleright$ Featurewise OOD Indicator

$\nabla_{l_{ti}}G_{ti}(l_{ti},\mu_{i}^{*})$$\triangleright$ Featurewise
Gradients

– Begin Forward Pass –

while $t\leq T$ do Update $\mu_{i}$ using Equation (2)

end while

Set $\mu_{i}^{*}$ to be the last iterate of $\mu_{i}$

OOD Detection. Compute $G_{ti}$ in Equation (4)

– Begin Backward Pass –

Jacobian Free Backpropagation. Output gradient
$\nabla_{l_{ti}}G_{ti}(l_{ti},\mu_{i}^{*})$ by computing derivative of
composition of log-sum-exp and ReLU functions in Equations (3) and (4) using
Chain rule.

GEM score for Efficient Backpropagation. So far, our forward pass computes the
optimal means $\mu^{*}$ and covariances $\Lambda^{*}$ of the GMM approximation
of the ID feature density function using a fixed point or network operator.
Having found $\mu_{i}^{*},\Lambda_{i}^{*}$, we can view the initial part of
our OOD network operator $\mathcal{N}_{ood}$ as the EM algorithm that outputs
the optimal parameters of ID feature density. But EM viewed as an operator
from $\mathbb{R}^{d}\to\mathbb{R}^{d}$ (mapping $l_{ti}\in\mathbb{R}^{d}$ to
$\mu_{i}^{*}\in\mathbb{R}^{d}$) makes backpropagation tricky since the
Jacobian of such a map will be a $\mathbb{R}^{d\times d}$ matrix, practically
infeasible for training purposes even when $d\approx 100$ is not very large.
The final ingredient we need for samplewise, and memory efficient forward pass
is to be able to compute a score for each $l_{ti}$ language feature, for which
we rely on the recent statistical developments in OOD detection, that have
already been tested on some simple classification applications. We use the
recently introduced GEM score in [31] to filter anomalous triplets to obtain
memory efficient gradients, shown in Figure 1. Given an derived feature from a
knowledge triple $ti\in T^{cn}$, its GEM score is defined using a log-sum-exp
energy function as,

$\displaystyle
s_{ti}=\log\sum_{i=1}^{k}\exp{(-\frac{1}{2}(l_{ti}-\mu_{i}^{*})^{T}\Lambda_{i}^{-1}(l_{ti}-\mu_{i}^{*}))}$
(3)

where $l_{ti}\in\mathbb{R}^{d}$ is the language feature of external triplets,
$\mu_{i}^{*}\in\mathbb{R}^{d}$ is the output of the network operator
$\mathcal{N}_{ood}$ (as in Eq (1)) of ID triplets $\\{t_{1},...t_{k}\\}\in T$
and $\Lambda^{*}\in\mathbb{R}^{d\times d}$ is the covariance matrix of ID
triplets. The corresponding OOD detection $G_{ti}\in\\{0,1\\}$ using Eq3 is
given by simple thresholding,

$\displaystyle G_{ti}=1,$ $\displaystyle\quad\text{if }s_{ti}\geq\lambda_{1},$
(4)

where $\lambda_{1}$ denotes a threshold parameter, when $G_{ti}=1$, $l_{ti}$
will be concatenate to ID triplets, otherwise to negative image-text pairs.
$\mu_{i}$ is updated during the training process, so $s_{ti}$ is a trainable
parameter to better capture uncertainty. We can define a OOD procedure for
$v_{m}$’s similarly.

Interaction via Outlier Detection. For applications that require a high number
of patches $P$ (or concepts $cn$), the likelihood that one of the patch
features or text features to be an outlier also increases dramatically. In
high dimensional settings, this can increase the training time taken by first
order methods significantly, especially when minibatches are used to compute
gradients [13]. Alternatively, when features $l_{n}$ (or $v_{m}$) are
computationally easy to extract, say using a GAN, it is reasonable to expect
that a certain fraction of the $l_{n}$ or $v_{m}$ are outliers, and should not
be used for backpropagation purposes. In a more optimistic scenario, we may
want to customize our predictions, and handle “on-the-fly” integration of
explicit knowledge. In our framework, this corresponds to treating $\mu_{i}$
in (3) as trainable parameters. We can update the initialization $\mu_{i}$
without storing the trajectory, or forming the full Jacobian which can be
expensive, as in our Algorithm 1.

###  3.2 Multimodal Training using ID Concepts

Now we will explain how to setup the overall training objective using the OOD
indicators $G_{ti}$. For this, we follow standard procedures in which the
features from modalities are matched. First, we compute pairwise signed scores
$s_{mti}\in(-1,+1)$ by applying elementwise nonlinearity. These scores
$s_{mti}$ are then used to obtain unsigned weights $\alpha_{mti}\in(0,1)$ by
applying a SoftMax operation with an inverse temperature $\lambda_{2}>1$ (see
[7] for more details). That is for each image-language feature pair
$v_{m},l_{ti}$, we set,

$\displaystyle\begin{split}s_{mti}=\text{GeLU}\left(\frac{v_{m}^{T}l_{ti}}{\|v_{m}\|\|l_{ti}\|}\right),\alpha_{mti}=\textbf{softmax}(s_{mti})\end{split}$
(5)

where $\text{GeLU}(x):=x\mathbb{P}\left(\mathcal{N}(0,1)\leq x\right)$, here
$\mathcal{N}(0,1)$ is the standard normal distribution, and softmax returns a
distribution over $ti$ for each image patch $m$, that is, each
$\alpha_{m}\in\mathbb{R}^{T^{cn}}$ is a nonnegative vector, and sums to 1. In
our implementation, we simply reweigh encoded language features using a vision
information from image encoders. Specifically, we reweigh the text features
$l_{ti}$ by summing over all the unsigned weights to obtain

$\displaystyle
a_{ti}^{t}=\sum_{m=1}^{P}{\alpha_{mti}l_{ti}}=l_{ti}\sum_{m=1}^{P}{\alpha_{mti}},$

Finally, we calculate the similarity between these reweighed language features
$a_{ti}^{t}$, and vision features $v_{m}$ as,

$\displaystyle
R(v_{m},a_{ti}^{t})=\frac{v_{m}^{T}a_{ti}^{t}}{\|v_{m}\|\|a_{ti}^{t}\|},m\in[P].$
(6)

Intuitively, for a test or unseen image-caption sample, a large value of $R$
in equation (6) indicates that patch $m$ and concept $ti$ are likely to occur
together for a fixed set of learnable parameters. With training data given as
$(v_{m},l_{ti},y_{mti})$, we can simply use such similarity based loss
functions even under the presence of outliers with the help of $G_{ti}$
computed using our OOD Layer 1. Hence, the loss function we use for concepts
$ti$ in our VK-OOD architecture can be written as,

$\displaystyle\mathcal{L}_{\text{ood}}=\mathbb{E}_{(V,L_{KG})}G_{ti}\mathcal{H}(y_{mti},R(v_{m},l_{ti})),$
(7)

where $L_{KG}$ is the distribution of language features obtained from external
knowledge, $\mathcal{H}$ denotes the usual cross-entropy function, $y_{mti}$
corresponds to the matching label of image and extracted knowledge triplets.

Overall Training Objective. We use our external knowledge based loss in
equation (7) with two standard matching based loss functions commonly used in
Multimodal training pipelines. First, we use Image Text Matching (ITM) loss
$\mathcal{L}_{\text{ITM}}$ defined as,

$\displaystyle\mathcal{L}_{\text{ITM}}=\mathbb{E}_{(V,L)}\mathcal{H}(y_{mn},R(v_{m},l_{n}))+\mathcal{L}_{\text{ood}},$
(8)

and second, we use Masked Language Model (MLM) loss $\mathcal{L}_{\text{MLM}}$
defined as,

$\displaystyle\mathcal{L}_{\text{MLM}}=\mathbb{E}_{(V,\hat{L})}\mathcal{H}(y_{mn},R(v_{m},\hat{l}_{n}))+\mathcal{L}_{\text{ood}}$
(9)

where $\hat{L}$ denotes the distribution of language features obtained from
masked tokens. The total loss function $\mathcal{L}$ of our VK-OOD model we
propose is a linear combination of the ITM and MLM loss in equations (8), and
(9) is given by,

$\displaystyle\mathcal{L}=\mathcal{L}_{\text{ITM}}+\lambda_{3}\mathcal{L}_{\text{MLM}}$
(10)

where $\lambda_{3}>0$ is a regularization parameter.

######  Remark 1.

Note that, $s_{ti}$ of the same triplets in different training objectives may
vary on random masks. Therefore, we add $\mathcal{L}_{\text{ood}}$ in both Eq
8 and Eq 9. We also provide details of our pre-train objectives with OOD loss
in Appendix B.

##  4 Experiments on Downstream Tasks

In this section, we introduce datasets and implementation details in pretrain
and finetune settings. To evaluate our model, we conduct experiments on
multiple downstream tasks, including (1) visual question answering, (2)
natural language for visual reasoning, and (3) image-text retrieval.

###  4.1 Implementation details

Datasets. We pre-train on three datasets, including COCO [23], Visual Genome
[21], and SBU Captions [34] with total of 1M images and 6.8M image-caption
pairs, as approximate 30$\%$ less than baselines. Each caption is parsed to 1
- 3 triplets and augmented with 5 external knowledge triplets. For downstream
datasts, we use Flickr30k [35] and COCO for image-text retrieval, VQAv2 [2]
and OKVQA [28] for visual question answering and ablation studies, and NLVR2
[41] for visual reasoning. We resize each image to the size of $224\times 224$
by center-cropping. In the merged attention module, each multimodal encoder
layer consists of one multi-head self-attention block and one feedforward
block, and total number of identical layers is 12. For downstream tasks, we
fine-tune with base learning rate of 5e-6 on higher resolutions of image with
size of $480\times 480$ to obtain better performance.

Encoder backbones. First, we retrieve explicit knowledge triplets in pre-
processing, by using ConceptNet
Numberbatch111https://github.com/commonsense/conceptnet-numberbatch. Next, we
use RoBERTa [24] and CLIP-ViT-B32 by [36] as text encoders. For the image
encoder, we use CLIP-ViT-B32 [36] and Swin-Base [25] as backbones.

Network training. For the pre-training, we use AdamW optimizer designed by
[26] with the base learning rate of 1e-5 for image and text encoders, and 5e-5
for multimodal module. We pre-train and fine-tune on 8 NVIDIA RTX 2080Ti GPUs.
The warm-up ratio of learning rate is 10% of the total training steps, and the
learning rate was decayed linearly to 0 in the rest steps.

Table 1: Overall performance and comparison with other models on multiple downstream tasks. “B" denote base models. Here, we choose to compare with similar pre-train dataset size models. The bold values mean the best model in the table. Our model outperforms baseline models in all downstream tasks. Model | VQAv2 | NLVR2 | COCO
---|---|---|---
IR R@5 | TR R@5
UNITER-B | 72.7 | 75.8 | 78.5 | 87.4
ViLT-B | 70.3 | 74.6 | 72.0 | 86.2
ALBEF(4M) | 74.5 | 80.5 | 81.5 | 91.4
VinVL-B | 75.9 | 83.1 | 83.2 | 92.6
PixelBERT | 74.5 | 77.2 | 77.5 | 87.5
FLAVA | 72.8 | 79 | - | -
VK-OOD | 76.8 | 83.9 | 83.6 | 93.1

###  4.2 Results on Downstream Tasks

We evaluate our VK-OOD models on common vision-language downstream tasks. We
finetune our model for 10 epochs with base learning rate of 1e-5 for all
downstream tasks. In addition, we apply RandAugment [8] as augmentation
strategy in finetuning steps.

Takeaway. In all the experiments, we establish that our VK-OOD model achieves
the best performance comparing with six SOTA vision-language models on three
downstream tasks with several datasets while using lower number of parameters
(see Table 1).

Visual Question Answering (VQA). For VQA tasks, the goal is to predict answer
classes as a classification problem. We finetune our model on VQAv2 train sets
and use the validation images and their questions pairs for internal
validation. Then, we evaluate this task on the VQAv2 test set. The VQAv2
dataset results 222https://eval.ai/challenge/830/overview are shown in Table
1. Our model VK-OOD outperforms all the baselines on this dataset and produces
76.8% accuracy.

Natural Language for Visual Reasoning. This task is to predict whether a text
description is matched to a pair of images. We evaluate our model along with
the baseklines models on the NLVR2 dataset for this task. We finetune our pre-
train model with the pair method that we concatenate the features of each pair
(question, one image) extracting from VK-OOD and predict outputs as a binary
classifier. Table 1 shows the results. Our model VK-OOD achieves the best
result in terms of accuracy with 0.8% and 9.5% increase than the best and
worst performing baselines respectively. The performance of our model shows
the ability of visual reasoning while incorporating implicit (e.g., semantic
information) and explicit knowledge (e.g., knowledge graph).

Image-text Retrieval. The tasks include two different types: i) to retrieve
images from text queries, and ii) retrieving texts from images. We evaluate
our model along with the baseline models on the COCO and F30K dataset. Our
model produces the best performance and outperforms the best and worst
performing baselines by up to .5% and 11.6% respectively on the COCO dataset
(Table 1). In other settings and on F30K dataset, the results are similar. The
details are given in the Appendix (see Sec. C.1)

Figure 2: Visualization of the attention maps of image and knowledge concept
triplets alignment. The results are from our VK-OOD model. The original sample
caption is “A man riding a bicycle down a city street". We highlight areas in
the example image corresponding to different knowledge triplets. Comparing
with the attention maps of the baseline model, our model learns more objects
and localize those objects correctly. Note that, the baseline model is trained
winthout KG and OOD components.

###  4.3 Qualitative Analysis

Fig. 2 is an example of multimodal alignment results from our VK-OOD comparing
with the model (baseline) without knowledge graph representations and OOD
detection layer. We use Grad-cam [38] to visualize the multimodal maps of the
pre-train models on image corresponding to knowledge triplets. As shown in
Fig. 2, our pre-train model has the capability to attend to the extracted
knowledge concepts, such as buildings and cars. Thus, our model can detect
more objects to provide the ability for answering open questions. By contrast,
the baseline model is not able to distinguish objects which are not appearing
in the captions. Furthermore, we discuss more user studies on interactive OOD
detections by feeding in domain knowledge with different distributions in the
Appendix C.1.

Figure 3: Example case studies with OK-VAQ dataset. We show retrieved explicit
knowledge triplets. The number of example triplets is 2. Our model is able to
detect outliers of retrieved triplets shown in the first row examples. The
predicted answers are from our proposed VK-OOD model finetuning on OKVQA
dataset. Comparing with the baseline results, our model provides more correct
answers. Note that, the baseline model is trained without KG and OOD
components. Figure 4: Visualization of the multimodal feature space. $n$
denotes the number of clusters.

We demonstrate a case study with our proposed VK-OOD model on OK-VQA dataset,
and visualize various results in Fig. 3. We show the extracted knowledge
triplets based on the captions. For instance, ⟨apple, used for, making apple
pie⟩ is useful to obtain correct answers comparing with the baseline model.
This observation validates that explicit knowledge provides more reasoning
capability than implicit knowledge. Moreover, our model detects OOD triplets
by interacting with visual modality, i.e, the apple is fruit in image, thus is
not used for computing. The last one is a failure case of our model, because
the ground truth answer is abstract. Therefore, it brings our attention to
consider more inference and reasoning abilities in multimodal analysis.

##  5 Ablation Study Experiments

We study the effectiveness of different components (knowledge triplets and OOD
detection layer) from VK-OOD via several experiments. Here, we perform the
following four different ablation studies: (i) the performances of different
combinations of the components in our proposed model, VK-OOD (Sec. 5.1), (ii)
the impact of different knowledge triplets (Sec. 5.2) (iii) the impact of
different knowledge encoders (Sec. 5.2) and (iv) the robustness of the OOD
layer (Sec. 5.3). Furthermore, we experiment with different backbones for
image-text retrieval tasks (please see C.1).

###  5.1 Ablation Study on VK-OOD Components

To compare the impact of the proposed components in our VK-OOD model, we
consider different combinations of inclusion and exclusion of knowledge graph
representations (KG) and out-of-distribution detection layer (OOD). The
results are shown in Table 2. The results show that our model achieves the
best performance when both the components are included in the model. Compared
to the other settings, it produces Moreover, comparing the results on VQAV2
and OKVQA datasets, the results imply that only the external knowledge
triplets (KG) can be beneficial to improve the performance especially on the
visual question answering task. Furthermore, using OOD layer solely has good
performance. This shows that including OOD layer in our model is helpful and
able to capture the noise of multiple modalities, such as missing or
mismatching modalities.

Table 2: Ablation studies of different components of our proposed models. “KG" and “OOD" denote knowledge graph representations and out-of-distribution detection layer respectively. The setting with both components outperforms other settings on all datasets and downstream tasks. Method | Downstream tasks
---|---
KG | OOD | VQAV2 | OKVQA | NLVR2
|  | 73.9 | 45.5 | 80.6
✓ |  | 74.6 | 48.3 | 81.8
| ✓ | 74.1 | 46.2 | 81.1
✓ | ✓ | 76.8 | 52.4 | 83.9

(a)

(b)

Figure 5: Ablation studies results on OKVQA dataset. (a) Results on accuracy
with different numbers of external knowledge triplets (blue) and the number of
clusters (orange) in the GMM process (see Eq. 2). The accuracy increases with
the increase in the number of triplets. (b) Results on the robustness analysis
of the OOD layer with different level of data incompleteness. The green line
and blue line are the normalized Hamming distance of $G_{ti}$ (left) and VQA
Accuracy (right). The green line shows the hamming distance increases with the
amount of incompleteness in the data.

###  5.2 Ablation study on Knowledge Graphs

We conduct multiple ablation studies on different numbers of extracted
knowledge triplets as well as different knowledge encoders.

Numbers of external triplets. To analyze the model performance, we conduct
experiments to explore the impact of the amount of the knowledge triplets. We
evaluate this on visual question answering tasks using OKVQA dataset. Fig. 5a
shows the experimental results. Unsurprisingly, increasing the number of
retrieved knowledge triplets improve the accuracy of predicted answers. We
achive the best accuracy of $52.4\%$ when the number of triplets is 5.

Knowledge Encoders. We also evaluate different knowledge encoders, i.e.,
different embeddings of implicit or/and explicit knowledge. Table 3 shows the
superiority of our model with different encoders. Our model produces 1.8% and
19.7% more accurate results than the best and worst performing baselines
respectively. Although using ConcepNet embeddings solely, our multimodal
training pipeline also learn implicit knowledge in the multimodal fusion
encoder. Moreover, we compare our model performance with the models using
external knowledge resources. Our proposed model takes advantages of implicit
knowledge from large-scale vision-language pretrained models and integrating
explicit knowledge prior information. Therefore, we outperform other models
using external knowledge resources.

Table 3: Experimental results on varying the different knowledge encoders on OKVQA dataset. In the literature, these are the baselines that are evaluated on only OKVQA dataset. “B" denotes base models, “CN" denotes ConceptNet, and “GI" denote Google Images. Our model VK-OOD outperforms the baselines on the OKVQA dataset. Method | Knolwedge resources | OKVQA
---|---|---
ConceptBERT | CN | 33.7
KRISP | Wiki + CN | 38.4
MAVEx | Wiki + CN + GI | 39.4
KAT-B | Wiki + GPT3 | 50.6
UnifER | CN + ViLT | 42.1
VK-OOD | CN | 51.1
CN + BERT | 52.4

###  5.3 Robustness of the OOD Layer

Incomplete Knowledge Triplets. To evaluate the sensitivity to OOD detection
performance, we conduct experiments of incomplete knowledge triplets with
missing values. Fig 5b shows the results. The green line and blue line are the
normalized Hamming distance of $G_{ti}$ (left) and VQA Accuracy (right). Note
that, hamming distance of $G_{ti}$ encodes the amount of out-of-distribution
knowledge triplets in the data. The green line shows the hamming distance
increases with the amount of incompleteness in the data. On the other hand, as
expected, the blue line shows that the performance of the model decreases with
the amount of incompleteness in the data. However, even with $30\%$ incomplete
data, our model achieves higher accuracy ($51.2\%$) than the best baseline
model ($50.6\%$). Moreover, we measure the impact of the OOD layers on the
pretraining objectives. The results are given in the Appendix (see C.1).

Number of Clusters.  We explore impacts of the number of clusters in
optimizing the GMM process and the results are shown in Fig 5a. Note that we
present an empirical analysis on the same in Fig. 5a above with the blue line.
The general trend is that the performance improve with the number of clusters.
To further justify the quality of the learned representation in our model, we
illustrate the learned output features by U-MAP [29] in Fig. 4. Here, we
present the feature embedding spaces of multiple modalities on COCO dataset.
Different colors indicate different clusters. We show example images of the
clusters in Appendix C.2. Comparing with the baseline, the results demonstrate
that more clusters can be identified over the multimodal features extracted by
our VK-OOD model. We also show feature maps with different number of clusters
in our optimization setups in Fig. 4. It implies that our model is able to
detect outliers more accurately.

##  6 Discussion

Limitations.  In this work, we mainly conduct experiments on discriminative
tasks using the encoders only, while generative tasks are still left to be
observed. We will extend our model with encoder-decoder architectures to
explore the model capabilities on visual question and caption generation
tasks. Since we have shown that explicit knowledge can be supervision in
vision-and-language training, we believe that different knowledge bases such
as medical knowledge graphs are able to provide the user desired domain
distributions. We leave these applications and comparisons for future
empirical studies.

Conclusions.  We present a training framework to facilitate multimodal
analysis under distribution shifts and/or the presence of outlier
distributions within the input sample space. There have been various other
proposals that either design or exploit special structure in one (or many) of
the available modalities for faster pretraining purposes mentioned in [45],
and for egocentric vision tasks [47]. While the approaches have been shown to
perform well in large-scale settings, from an end-to-end pipeline, those alone
may be insufficient – for example, many frames in a video have low semantic
information content and may require complex processing pipelines [5]. Hence,
we believe handling outliers in the context of multimodal analysis is an
important topic as more models get integrated or fused. Moreover, none of the
models proposed allow for interaction to detect, and (soft) filter them in a
computationally efficient way. Naturally, our proposed OOD layer can be
directly instantiated within such complex pipelines, while allowing us
possibly intervene, and accelerate the training process. We show extensive
empirical analysis on various setups asserting that OOD detection in the
training pipeline can be extremely effective in downstream tasks.
Specifically, we achieve significant training time savings in all our
experiments while preserving the state-of-the art performance with respect to
many qualitative, and quantitative evaluation metrics.

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##  Appendix A More details of Encoders

###  A.1 Image Encoder

In this work, we focus on patch features and apply vision transformer based
models(ViTs) by [43] as our visual encoder backbones. We split input image
into a squence of patches and adopt the linear projection embedding of patch
features $V$, which simplifies the step for fusing with text embedding. We
pre-train our model with multiple popular ViTs to examine the influence of
image encoder in OOD detection backpropagation process.

###  A.2 Knowledge Encoder

Given the caption $S$, we parse it into triplets in the form of
$T^{id}=\langle o(c),r(c),o^{\prime}(c)\rangle$, where $o(c)$ and
$o^{\prime}(c)$ are concepts $\in C^{id}$ ,and $r(c)$ is the relation(s)
between them, i.e., ⟨man, riding, bicycle⟩. In our example, the seed
triplets(ID triplets) parsed from the caption are $\langle\text{man, riding,
bicycle}\rangle$ and $\langle\text{bicycle, down, street}\rangle$. Then we
construct knowledge graph by bridging these triplets with external open
knowledge including domain and commonsense knowledge graphs, e.g., ConceptNet
[40]. ConceptNet provides a large scale commonsense knowledge with over 21
million edges by 36 type of relations connecting 8 million nodes, i.e., IsA,
UsedFor, AtLocation. In this study, to complete our knowledge graph, we
collect concepts by querying from ConceptNet using $o(c)$, $o^{\prime}(c)$ and
$rel_{i}$ where $i\in[0,36]$ and integrate extracted triplets to seed
triplets. For example, given “street" as $o(c)$ and “AtLocation" as $rel_{i}$,
we will extract the related concepts are located at street to form triple
$t_{i}$. Specifically, we query explicit knowledge triplets of $o(c)$ and
$o^{\prime}(c)$ from ConceptNet to form $T^{cn}$, i.e., ⟨bicycle, used for,
transport⟩. Finally, these knowledge triplets $\in T=T^{cn}\cup T^{id}$ are
encoded as language features using a language encoder.

###  A.3 Multimodal Fusion Encoder

Considering the above mentioned image $I$ and caption $S$ as inputs, we
perform a two-stream transformer pipeline consisting of stacked multiple
layers to joint vision and concept text representations. For each layer, we
have self-attention unit and merged cross-attention unit which integrates
vision and knowledge semantic information and the alignments across them, and
a positionwise feed-forward network.

As the standard transformer architecture in [43], the attention function
computes identical learnable parameters (weights) as in Eq. 11 and Eq. 12 ,
where d is the dimension of the inputs, a query Q, key K, and value V. We use
fusion encoder recursively comparing similarity among the image-text pairs as,

$\displaystyle\begin{split}\text{Attn}\left(Q_{I},K_{L},V_{L}\right)=\\\
\text{softmax}\left(\frac{Q_{I}K_{L}^{T}}{\sqrt{d}}\right))V_{L},\end{split}$
(11)

and

$\displaystyle\begin{split}\text{Attn}\left(Q_{L},K_{I},V_{I}\right)=\\\
\text{softmax}\left(\frac{Q_{L}K_{I}^{T}}{\sqrt{d}}\right))V_{I}\end{split}$
(12)

where I and L denote image modality and language modality respectively.

Moreover, we update the image and language embedding outputs of themselves
previous layer as queries and concatenate them together as keys and values. To
further improve the performance of attention function, we use a multi-head
attention which is composed by multiple paralleled attention function in Eq.
11 and Eq. 12 in each head. The feed-forward layer transform the outputs of
multi-head attention through two fully-connected layers with GeLU activation.

##  Appendix B Pre-train objectives

We introduce our pre-train objectives in our pipeline in this section,
including image text matching (ITM) and masked language modeling (MLM).

###  B.1 Image Text Matching

To incorporate both the vision and the language representations, we adopt ITM
which is widely used in previous VL studies. Given an image and text of triple
pair $\langle v_{m},l_{n}\rangle$, ITM predicts whether they are matched as
positive examples or not, and it is a binary classification problem with the
loss function in Equation 8. We assume that each image and ID triple pair
$\langle v_{m},l_{n}\rangle$, as a positive example. The negative pairs are
constructed through batch-sampling.

###  B.2 Masked Language Modeling

MLM utilizes vision features and text features of ID concepts and relations to
predict the masked tokens in the caption sentence $S$. Following most VL
models, we randomly masked some tokens in $S$ replacing as $y^{msk}$ and
predict them with their visiual and textual features.

##  Appendix C More Experiments

In this section, we show more experiments on ablation studies and qualitative
analysis of our proposed VK-OOD models.

Table 4: Ablation study experiment results of VK-OOD model. Baseline denotes vision and knowledge multimodal without explicit knowledge and OOD detection layer. ITM is image-text matching, and MLM is masked language modeling. Results on VQA are on test-dev set. Both downstream results are in zero-shot settings. The bold values mean the best model in the table. Comparing with the baselines, our model with OOD detection layer outperforms on all objectives with two datasets. Training on combinations of objectives improves model performance. Model | Objectives | VQA | Flickr30k
---|---|---|---
test-dev | TR@1 | IR@1
Baseline | ITM | 70.6 | 82.1 | 65.6
Baseline | MLM | 72.8 | - | -
Baseline | ITM+MLM | 74.2 | 88.1 | 74.1
VK-OOD | ITM | 72.1 | 84.5 | 69.8
VK-OOD | MLM | 73.4 | - | -
VK-OOD | ITM+MLM | 74.8 | 89.0 | 77.2

###  C.1 Ablation Studies

To evaluate our proposed model, we perform more ablations with the default
pre-training settings of the baseline and our model mentioned in Section 3.3
of the main paper. We consider different combinations of pre-train objectives
in zero-shot settings.

Pretraining Objectives with OOD Layer. We observe our model performance on
pre-training objectives. Our raw results are presented in Table 4. We train on
pre-train datasets with $\mathcal{L}_{\text{ITM}}$ in Equation 7,
$\mathcal{L}_{\text{MLM}}$ in Equation 8 and $\mathcal{L}$ in Equation 9. The
results in Table 4 show that training on image-text matching and masked
language modeling is beneficial for both downstream tasks comparing to the
baseline model, especially, there is promising improvements in image retrieval
and text retrieval tasks. Thus, it is beneficial to train on both ITM and MLM
for filtering outlier concepts and improve performance on downstream tasks.

Backbones on downstream tasks. Since different backbones of image and language
encoders may affect model performance, we compare the difference backbone
combinations on image and text retrieval tasks on COCO dataset. The results
are shown in Table 5. We observe that CLIP-ViT as vision encoder and RoBERTa
as text encoder outperforms other combinations.

###  C.2 Qualitative Analysis

Comparing the the baseline, our model result demonstrates more clusters can be
identified over the multimodal features extracted by VK-OOD. We also show
feature maps with different number of components in our optimization setups in
Figure 4. Therefore, it illustrates that our model is able to detect outliers
and cluster images closest to the corresponding $\mu_{i}$ with image and
explicit knowledge triplets. Figure 6a and Figure 6b are examples that the
nearest images in each cluster.

Figure 7 and Figure 8 show more qualitative examples of our multimodal
alignment results of our pre-train models. We visualize the multimodal
attention maps on images corresponding to concept triplets using Grad-cam
designed by [38]. Following our model architecture, the caption is parsed and
integrated with knowledge triplets. The right bottom subfigure in our model in
Figure 7 and Figure 8 are the multimodal alignment of original captions from
MSCOCO [23] dataset. Other subfigures show the alignments of extracted
triplets on the image.

Interestingly, we find that our model is able to capture concept “plug” as a
part of “refrigerator” or “microwave” in Figure 7. The heatmap area of “plug”
and “microwave” in Figure 7 clearly suggest that our model has the capability
to exploit different relevance between visual and corresponding conceptual
text features. By contrast, the baseline results have not shown the relation
between plug and microwave. In Figure 8, it shows that we detect three zebras
comparing with baseline, but counting cars is not performing well as we
expected – since the size (or scale) of cars is not sufficiently high, and
moreover some parts of them are occluded.

Table 5: Ablation studies on different backbones on image-text retrieval tasks. “B" denotes base model. We compare different backbones performance on two datasets. Our model with CLIP-ViT and RoBERTa achieves the best results. Comparing with other baseline, all backbones outperform baselines in term of R@1 on COCO dataset. Backbone | COCO
---|---
Vision | Language | Text retrieval | Image retrieval
R@1 | R@5 | R@10 | R@1 | R@5 | R@10
UNITER-B | 64.4 | 87.4 | 93.1 | 50.3 | 78.5 | 87.2
ViLT-B | 61.8 | 86.2 | 92.6 | 41.3 | 72.0 | 82.5
ALBEF(4M) | 73.1 | 91.4 | 96.0 | 56.8 | 81.5 | 89.2
PixelBERT | 63.6 | 87.5 | 93.6 | 50.1 | 77.6 | 86.2
Swin | RoBERTa | 72.1 | 93.2 | 95.9 | 51.6 | 78.3 | 88.2
Swin | CLIP-ViT | 73.8 | 91.4 | 96 | 52.4 | 81.3 | 90.1
CLIP-ViT | CLIP-ViT | 69.8 | 87.5 | 93.6 | 48.8 | 78.5 | 82.5
CLIP-ViT | RoBERTa | 74.7 | 93.1 | 96.8 | 57.9 | 83.6 | 92.8

(a) Cluster 1

(b) Cluster 2

Figure 6: Example images in each example cluster on COCO val set. Figure 7:
Visualization of the attention maps of image feature $v_{m}$ and language
features $l_{ti}$ knowledge concept triplets alignment. The results are from
our VK-OOD model. The original sample caption is “a giraffe and zebras mingle
as cars drive out of an animal park". We highlight areas in the example image
corresponding to different knowledge triplets. Comparing with the attention
maps of the baseline model, our model learns object shapes such as zebras and
localize those objects correctly. Note that, the baseline model is trained
winthout KG and OOD components. Figure 8: Visualization of the attention maps
of image and knowledge concept triplets alignment. The results are from our
VK-OOD model. The original sample caption is “a metallic refrigerator freezer
next to a microwave oven". We highlight areas in the example image
corresponding to different knowledge triplets. Comparing with the attention
maps of the baseline model, our model learns the relations between the parts
(i.e., plug) of the objects correctly. Note that, the baseline model is
trained winthout KG and OOD components.