# Unsupervised Person Re-identification via Simultaneous Clustering and
Consistency Learning

Junhui Yin, Jiayan Qiu, Siqing Zhang, Jiyang Xie, Zhanyu Ma∗, and Jun Guo

###### Abstract

Unsupervised person re-identification (re-ID) has become an important topic
due to its potential to resolve the scalability problem of supervised re-ID
models. However, existing methods simply utilize pseudo labels from clustering
for supervision and thus have not yet fully explored the semantic information
in data itself, which limits representation capabilities of learned models. To
address this problem, we design a pretext task for unsupervised re-ID by
learning visual consistency from still images and temporal consistency during
training process, such that the clustering network can separate the images
into semantic clusters automatically. Specifically, the pretext task learns
semantically meaningful representations by maximizing the agreement between
two encoded views of the same image via a consistency loss in latent space.
Meanwhile, we optimize the model by grouping the two encoded views into same
cluster, thus enhancing the visual consistency between views. Experiments on
Market-1501, DukeMTMC-reID and MSMT17 datasets demonstrate that our proposed
approach outperforms the state-of-the-art methods by large margins.

##  1 Introduction

Convolutional Neural Networks (CNNs) have undergone unprecedented success in
visual representation learning recently. To realize rich representation, CNNs
are typically trained using large-scale datasets (_e.g._ , ImageNet [8]) with
extensive human annotations. Despite their success, the large-scale data
annotation is costly and laborious, especially for complex data (_e.g._ ,
videos) and concepts (_e.g._ , image retrieval [7, 25, 45]). In contrast to
the methods [23, 19] that rely on heavy supervision, self-supervised learning
is another kind of technique, which can easily obtain the supervision signal
from the data itself and employ it to facilitate representation learning
without requiring extensive human annotation supervision.

Figure 1: a: Visual consistency from two different encoded views of the same
image based on network with Dynamic dropblock layer (DDL). b: Temporal
consistency from the same encoded view at different stages (_i.e._ ,
iterations). c: Visual and temporal consistency from different views at
different stages.

A fertile source of annotation-free supervision is video since the visual
world is continuous and does not change abruptly. Based on this, [37] uses
inherent consistency between observations adjacent in time as free supervisory
information for training model. Learning consistency meets the development of
the human visual system as infants can track and follow slow-moving objects
before mapping objects to semantic meanings. In this work, we focus on
constructing consistency in still person images for unsupervised
representation learning under the framework of person re-identification (re-
ID).

The requirement of re-ID is to match a person of interest with other images of
this person from non-overlapping cameras. Existing unsupervised re-ID
approaches [12, 27] first extract embedding features of unlabeled dataset from
network model and then apply unsupervised clustering to divide images into
different clusters for training model. These methods generate pseudo labels as
ground-truth labels by simply using clustering algorithm and thus have not yet
fully explored the semantic information existing in data itself, which limits
representation capabilities of learned models. In addition, the current
clustering-based methods only focus on target task of re-ID and easily latch
onto local information and low-level features (e.g., color, contrast, texture,
etc.), which is unwanted for the objective of semantic clustering [35].

To address the above problems, we design a pretext task for unsupervised re-ID
by learning consistency from still images, such that the clustering network
can separate the images into semantic clusters rather than focusing on low-
level features. Similar to previous unsupervised representation learning [37,
38], the aim of Consistency Learning (ConsLearn) is to learn feature
representations that support reasoning at visual and temporal consistency. To
construct the consistent information from single image and turn them into a
learning signal for optimizing network, we fisrt propose Dynamic Dropblock
Layer (DDL), which randomly drops the region of input maps, namely the
semantic body parts, to obtain differently semantical representations based on
the attentive feature learning of remaining regions. Then, we feed the same
person image into the model twice and apply DDL to convolutional feature maps
of the model to produce two encoded views (_i.e._ , feature representations)
of the image. Finally, the consistency across views is ensured by maximizing
agreement between two encoded features via a consistency loss in latent space.
The motivation behind ConsLearn lies in that there is inherent visual
consistency between the different views of the same images in space and the
predicted target region of training image can still backtrace to the initial
information by using the information of the other view regardless of losing
the target on a view.

The above pretext task integrates disparate visual percepts into persistent
entities and enhances visual consistency in feature space. To further enhance
the temporal consistency across different training stages (_i.e._ ,
iterations), we improve on existing methods by adopting the temporal average
model of network from the previous iterations to generate the invariance
feature for a view of image, which enables the consistency with the other view
obtained by the current encoder model, as shown in Figure 1.

Simultaneous clustering and consistency learning is one of the most promising
approaches for unsupervised learning of deep neural networks. As a pretext
task, ConsLearn can obtain the free supervision for consistency from the
unlabeled data and learn feature representations that supports identifying
consistency across views. As these features improve, the ability to predict
the missing patch of feature maps are enhanced, inching the model toward
consistency. As a target task, clustering process trains a deep neural network
using pseudo-labels and encourages network model to focus on the current task
of re-ID. The self-supervised task from representation learning requires
semantic understanding and thus induces clustering to obtain semantically
meaningful features rather than depending on low-level features, which is
present in existing clustering-based approaches [12, 27, 13].

The main contributions of this work can be summarized as follows:

$\bullet$ A novel Consistency Learning (ConsLearn) approach is proposed to
construct data association across encoded views from unlabeled pedestrian
images and learn representations for visual reasoning in a self-supervised
manner.

$\bullet$ ConsLearn is implemented with clustering, which not only perceives
the current pretext task, but also learns the feature information related to
the target task. In this way, our method can automatically separate images
into semantically meaningful clusters.

$\bullet$ The proposed method is applied on unsupervised person re-ID and
further extended to unsupervised domain adaptation (UDA) re-ID, and improves
the state-of-the-arts with significant margins.

##  2 Related Work

Unsupervised person re-identification. Unsupervised person re-identification
(re-ID) is very challenging to learn discriminative representation due to the
absence of target labels as learning guidance. Several methods [12, 27, 46]
resort to separate unlabeled data into different clusters by clustering
algorithms (_e.g._ , DBSCAN [11]) and iteratively trains network models based
on the generated pseudo labels. However, these methods highly rely on pseudo
labels and may be affected due to the hard quantization error caused by the
clustering error. To address this issue, some existing methods use some weak
supervised signal for training re-ID model. For instance, the tracklet-based
method [24] uses person tracklet as supervised information to jointly learning
within-camera tracklet correlation and cross-camera tracklet association,
without any additional annotations. Despite their success, these methods are
not truly unsupervised learning in essence and work hard when there are not
labeled data and weak supervised information (_e.g._ , tracklet). Another line
of this work is to employ deep re-ID model learned from labeled source data as
an initialized feature extractor and adapt the model with both source domain
and target domain to reduce the domain discrepancy between different datasets
on image-level [9, 39, 2] and attribute feature-level [29, 6]. [14] and [48]
use multiple network models and their corresponding mean nets to generate soft
pseudo labels for supervising other models. The main difference from them is
that we construct temporal consistency during training process by only using
one current network and its past (mean) network with DDL. In addition, many
other contrastive learning methods [18, 15] also using the momentum-based
averaging model.

Figure 2: Illustration of our proposed framework. During training, unlabeled
data are fed-forward into the deep re-ID network with DDL and its temporally
averaging model to obtain differently encoded views (_i.e._ , feature
representations). Subsequently, two components are designed to optimize the
network with unlabeled data, respectively. The first component is a clustering
module that provides the same cluster assignments for two encoded features to
calculates the cross-entropy loss. The second component is ConsLearn module
that learns vision and temporal consistency from still images via a
consistency loss.

Self-supervised representation learning. As a form of unsupervised learning,
self-supervised representation learning is a kind of techniques that learn
representations by solving a pretext task, where the supervision signal can be
obtained freely from the large amount of unlabeled data available online.
Contrastive learning [16] is the core idea in this line of investigation. It
has recently shown great progress [40, 33, 20, 1, 43]. As a milestone, MoCo
[18] maintains a queue of negative samples and train the model with a memory
bank to improve consistency of the queue. After that, considerable attention
has been focused in this direction [5, 34, 3, 15, 4] and spread over many
other tasks [42, 17]. Inspired by MoCo series [18, 5], we use the mean net to
construct temporal consistency during training process. Differen form these
works, our method is The main difference from MoCo series [18, 5] is our
method can be thought of as MoCo without negatives and rarely depend on large
batch, while MoCo highly relies on them.

Compared to still images, videos should be a fertile source for self-
supervised learning as they naturally possess the temporal resolution and
dynamics. A suitable supervision for free is inherent visual correspondence
between adjacent frames in video. [37] utilizes cycle consistency as free
supervision to learn fine-grained correspondences between pixels. [38] propose
a data association method, which concentrates more on learning high-level
semantic correspondences and is adaptive to the re-ID problem. However, this
technique is required to the prior knowledge of camera view labels, which
sometimes are unavailable. The above methods usually focus on learning
consistency between adjacent frames of video, while ours concentrate more on
how to find the consistency in still person images and utilize it to provide a
self-supervised solution for person re-ID task, without any additional
information (_e.g._ , cameral view labels).

##  3 Approach

In this section, we first propose the consistency learning (ConsLearn) as a
pretext task for unsupervised person re-ID. We then introduce the
discriminative clustering method and incorporate it with ConsLearn in a joint
optimization framework. The overview of the training procedure is illustrated
in Figure 2.

###  3.1 Consistency Learning

Clustering tends to focus on low-level features, like color, which is
unessential for the objective of semantic clustering. To explore richer visual
patterns and obtain semantically meaningful features, we propose ConsLearn and
integrate it with the clustering network. The following sections present the
cornerstones of our approach, which can encourage clustering network to
separate images into semantically meaningful clusters without human
annotations.

Dynamic dropblock layer. In this subsection, we present the details of dynamic
dropblock layer (DDL). DDL can be applied on any feature layer of network
model, and induce model to capture the different semantic information of the
object during training. It is worth noting that DDL is deactivated during the
testing phase.

The input of DDL is a convolutional feature map $F\in\mathbb{R}^{H\times
W\times C}$ where $C$ represents the number of channels, $H$ and $W$ are
height and width of feature map, respectively. We first set two main
hyperparameters $\alpha$ and $\beta$. They control the height and width ratio
of the erased region on map $F$, respectively. The size of region to be
dropped increases as $\alpha$ and $\beta$ increases and is adaptive to the
size of feature map when the hyperparameters are fixed. Therefore, the
dropblock layer is dynamic. The height and width of the erased region vary
from task to task. But in our work, the removed region should contain a
semantic part of feature map. Specifically, the drop mask
$M_{drop}\in\mathbb{R}^{H\times W}$ is generated by setting the pixel to 0 if
it is inside the generated erased region, and 1 otherwise. The DDL randomly
remove the part of feature map $F$ by multiplying it to the drop mask. In this
way, we can produce different views for input feature map, namely different
semantic body parts. We visualize the example of each component in the
following experiment. The regularization technique of Cutout [10] also studies
the application of random erasing. A difference from Cutout is the erased
region size of our DDL changes dynamically with the size of feature map, while
the removed region size of Cutout is fixed and squared. The other difference
is that our DDL can be applied on any feature layer of network instead of only
input image as in Cutout. In fact, our DDL can be considered as an extension
of random erasing for feature maps and also an extension of Cutout [10] or
Dropout [32].

Learning consistency. Consider as input a person images $x$, the pixel input
is mapped to a feature space twice by an encoder with DDL, such that
$h_{k}=\Phi_{k}(x|\theta)$, $k=1,2$. Here, $k=1,2$ mean the two data flows of
the same image, which are fed into network in order. Since the erased area of
DDL is given randomly at each layer, the firstly entered network
$\Phi_{1}(\cdot|\theta)$ is different from the second network
$\Phi_{2}(\cdot|\theta)$. Thus, $k$ is also used to distinguish them. This is
also why we use two data flows for the same image. In this way, the network
models $\Phi_{k}(\cdot|\theta)$ output two different encoded views $h_{k}$,
$k=1,2$, leading to two differently semantic features from the same image. Our
aim is to reduce the ability for clustering depending on low-level features
and induce the model to learn the inherent visual consistency across different
views. Therefore, we want to maximize the agreement between $h_{1}$ and
$h_{2}$. A linear layer $g$: $\mathbb{R}^{D}\rightarrow\mathbb{R}^{\Omega}$
follows the feature representations to map them to a latent space, which is
defined as

$\small u_{k}=g(h_{k}),k=1,2.$ (1)

In this space, our model can better learn a semantically meaningful
representation that describes what is in common between $u_{1}$ and $u_{2}$
while discarding instance-specific details.

The encoder model at current iteration can output the encoded views for all
the person images, but this rapidly changing encoder also reduces the feature
representations’ consistency across different training stages and eventually
results in training error amplification. To resolve this issue, we present the
momentum-based moving average model as a way of preserving more original
knowledge for unsupervised learning with a consistency loss. Formally, let the
temporal average model be $\Phi_{k}^{*}(\cdot|\theta^{*})$. Then, its model
parameter $\theta^{*(t)}$ at current iteration $t$ can be updated by

$\small\theta^{*(t)}=\zeta\theta^{*(t-1)}+(1-\zeta)\theta^{(t)},$ (2)

where $\zeta\in[0,1)$ is a momentum coefficient and the initial temporal
average parameters $\theta^{*(0)}$ is $\theta$.

Two momentum-based encoded views of the same images are generated by the
temporal average model $\phi$ and the linear layer $g^{*}$. That is,
$\tilde{u}_{k}=g_{k}(\Phi_{k}^{*}(x_{k})),k=1,2$. Considering the errors
caused by self-variation and large differences in values, the feature
distributions $u_{k}$ and $\tilde{u}_{k}$ are scaled up by a softmax
operation,

$\small
v_{kl}=\delta(u_{k})=\frac{e^{u_{kl}}}{\sum_{j=1}^{\Omega}e^{u_{kj}}},k=1,2,$
(3)
$\small\tilde{v}_{kl}=\delta(\tilde{u}_{k})=\frac{e^{\tilde{u}_{kl}}}{\sum_{j=1}^{\Omega}e^{\tilde{u}_{kj}}},k=1,2,$
(4)

where $v_{kl}(\tilde{v}_{kl})$ is the $l$-th element of
$v_{k}(\tilde{v}_{k})$. Now, the problem is how to turn the consistency
information into a learning signal for optimizing network models.

It is well known that the Kullback-Leibler (KL) divergence $\displaystyle
KL(\cdot)$ can be thought of as a measurement of how far the distribution $Q$
is from the distribution $P$. For discrete probability distributions $P$ and
$Q$ defined on the same probability space $\mathcal{X}$, $P$ typically
represents the “true” distribution of data, while $Q$ typically represents a
approximation of $P$. In order to find a distribution $Q$ that is closest to
$P$, we can minimize $KL$ divergence, which can be rewritten by

$\displaystyle KL(P\|Q)$
$\displaystyle=-\sum_{x\in\mathcal{X}}P(x)\log(Q(x))+\sum_{x\in\mathcal{X}}P(x)\log(P(x))$
(5) $\displaystyle=H(P,Q)-H(P),$

where $H(P,Q)$ is the cross entropy of $P$ and $Q$, and $H(P)$ is the entropy
of $P$. Here, $KL(P\|Q)$ is proportional to $H(P,Q)$ if $H(P)$ is a constant.

The embedding features $v_{k}$ and $\tilde{v}_{k}$, $k=1,2,$ can be
interpreted as the distribution of a discrete random variable over training
samples $x$. Furthermore, $\tilde{v}_{i}$ can be viewed as a constant or
“true” distribution since it represents invariant feature distribution
obtained by the temporally moving average model. Our aim is that $v_{1}$
should be close to $\tilde{v}_{2}$ and $v_{2}$ should be close to
$\tilde{v}_{1}$ as much as possible, which supports visual reasoning between
different semantic features. Therefore, $H(P,Q)$ is adopted to turn the
consistency information into a learning signal for optimizing our models. The
consistency of different encoded views is measured using the following loss,
as

$\displaystyle\mathcal{L}_{co}$
$\displaystyle=H(\tilde{v}_{1},v_{2})+H(\tilde{v}_{2},v_{1})$ (6)
$\displaystyle=-\sum\tilde{v}_{1}\log(v_{2})-\sum\tilde{v}_{2}\log(v_{1}).$

###  3.2 Unsupervised Person Re-identification by Clustering

Unlike pervious clustering techniques [12, 27, 13], our clustering method
first generates pseudo labels according to features from a view of the same
images and utilizes the same labels for all the encoded views since they share
large visual similarity. Its aim is to further enhance the visual consistency
between different views based on unsupervised clustering. Specifically, given
encoded features $\\{h_{1}^{i}\\}_{i=1}^{N}$ of images $\\{x^{i}\\}_{i=1}^{N}$
from a view, clustering algorithm (_i.e._ , DBSCAN [11]) divides these learned
features into different clusters as pseudo label $\\{\hat{y}\\}_{i=1}^{N}$,
which represents the image’s membership to one of $M$ possible predefined
categories. Meanwhile, the representations are also fed into a linear
classification layer, converting the feature vector $h_{1}^{i}$ into a vector
of class scores $z_{1}^{i}$. Finally, the softmax operator is performed to map
the class scores to class probabilities $p(\hat{y}_{i}|x_{i})$, which is the
predicted probability of image $x_{i}$ belonging to the identity
$\hat{y}_{i}$. As in [52], the clustering procedure for two encoded views is
achieved by minimizing the cross entropy loss,

$\small\mathcal{L}_{ce}=-\frac{1}{N}\sum_{i=1}^{N}\log
p(\hat{y}_{i}|h_{1}^{i})-\frac{1}{N}\sum_{i=1}^{N}\log
p(\hat{y}_{i}|h_{2}^{i}).$ (7)

Furthermore, we introduce a softmax-triplet loss to enforce a positive pair to
be closer and a negative pair apart from each other in the representation
space. As in [21], let $h_{1}^{(in)}$ and $h_{1}^{(ip)}$ denote the similarity
of the hardest negative pair $\\{h_{1}^{i},h_{1}^{n}\\}$ and the hardest
positive pair $\\{h_{1}^{i},h_{1}^{p}\\}$ for encoded views
$\\{h_{1}^{i}\\}_{i=1}^{N}$, with an anchor $h_{1}^{i}$. The similarity can be
computed as $h_{1}^{(in)}=-\|h_{1}^{i}-h_{1}^{n}\|$ and
$h_{1}^{(ip)}=-\|h_{1}^{i}-h_{1}^{p}\|$, where $\|\cdot\|$ represents
Euclidean distance. In this sense, minimizing the similarity of the hardest
negative pair means maximizing the distance between $h_{1}^{(i)}$ and
$h_{1}^{(n)}$ and vice versa. Similarity, encoded views
$\\{h_{2}^{i}\\}_{i=1}^{N}$ have similar results. Thus, the softmax-triplet
loss for two encoded views can be performed by

$\displaystyle\mathcal{L}_{st}=$
$\displaystyle-\frac{1}{N}\sum_{i=1}^{N}\log\frac{\exp(h_{1}^{(in)})}{\exp(h_{1}^{(in)})+\exp(h_{1}^{(ip)})}$
(8)
$\displaystyle-\frac{1}{N}\sum_{i=1}^{N}\log\frac{\exp(h_{2}^{(in)})}{\exp(h_{2}^{(in)})+\exp(h_{2}^{(ip)})}.$

Overall, unsupervised deep clustering alternates between clustering features
as pseudo label to update the parameters of the model using Eq. (7) and
learning features by collecting and distinguishing informative pairs
accurately using Eq. (8). The proposed procedure can be achieved by

$\displaystyle\mathcal{L}_{clu}=\lambda\mathcal{L}_{ce}+\xi\mathcal{L}_{st}$
(9)

The overall loss for network. ConsLearn is implemented with clustering, which
not only perceives the current pretext task, but also learns the feature
information related to the target task. By adding the clustering loss (Eq.
(9)) into the consistency loss (Eq. (6)), the overall loss $\mathcal{L}$ of
our final proposed method can be expressed as

$\small\mathcal{L}=\lambda\mathcal{L}_{ce}+\xi\mathcal{L}_{st}+\eta\mathcal{L}_{co},$
(10)

where $\lambda$, $\xi$ and $\eta$ are weighting parameters.

##  4 Experiments and Disscussions

###  4.1 Experimental Settings

Datasets and evaluation protocol. Our proposed model is evaluated on three
public person re-identification (re-ID) benchmarks: Market-1501 [50],
DukeMTMC-reID [31], and MSMT17 [39]. To evaluate the performance of our
method, two evaluation metrics are chosen as the mean Average Precision (mAP)
and the cumulative matching characteristic (CMC) top-1, top-5, top-10
accuracies [50].

Implementation details. All person images are resized to 256$\times$128 before
fed into the networks. Then, we adopt random cropping and flipping as the data
augmentation. After that, we feed these images into ResNet-50 [19] with the
last classification layer removed, initialized by the ImageNet [8]. We use a
mini-batch size of 256 in 4 GPUs. The network models are trained for 50
epochs, using ADAM optimizer with learning rate of 0.00035, weight decay of
0.0005. DBSCAN [11] is used for clustering in each epoch. For the momentum
coefficient, we set $\zeta$ as 0.999. We empirically set the hyperparameters
$\lambda,\xi$ and $\eta$ for loss balance as 0.2, 0.35 and 0.1 for
unsupervised re-ID on benchmarks including Market-1501 and DukeMTMC-reID. For
other datasets, these parameters may be slightly different.

Methods | Market | Duke | MSMT17
---|---|---|---
mAP | top-1 | mAP | top-1 | mAP | top-1
OIM [41] | 14.0 | 38.0 | 11.3 | 24.5 | - | -
BUC [27] | 38.3 | 66.2 | 27.5 | 47.4 | - | -
SSL [28] | 37.8 | 71.7 | 28.6 | 52.5 | - | -
MAR [44] | 40.0 | 67.7 | 48.0 | 67.1 | - | -
TAUDL [12] | 43.5 | 61.7 | 41.2 | 63.7 | 12.5 | 28.4
MMCL [36] | 45.5 | 80.3 | 40.2 | 65.2 | 11.2 | 35.4
HCT [46] | 56.4 | 75.2 | 45.1 | 63.2 | - | -
CycAs [38] | 64.8 | 84.8 | 60.1 | 77.9 | - | -
MMT∗ [14] | 74.7 | 88.4 | 60.8 | 75.0 | 25.8 | 57.2
Ours | 77.7 | 90.4 | 64.5 | 78.1 | 27.7 | 55.5
Table 1: Comparisons with state-of-the-art methods on three benchmarks for
unsupervised person re-ID task. “*” means the results are reproduced by
implementing the authors’ code. Bold text refers the best performance.

Methods |  Duke $\rightarrow$ Market |  Market $\rightarrow$ Duke
---|---|---
mAP | top-1 | top-5 | mAP | top-1 | top-5
PUL [12] | 20.5 | 45.5 | 60.7 | 16.4 | 30.0 | 43.4
SPGAN [9] | 22.8 | 51.5 | 70.1 | 22.3 | 41.1 | 56.6
HHL [51] | 31.4 | 62.2 | 78.8 | 27.2 | 46.9 | 61.0
UCDA [30] | 30.9 | 60.4 | - | 31.0 | 47.7 | -
PDA-Net [26] | 47.6 | 75.2 | 86.3 | 45.1 | 63.2 | 77.0
CR-GAN [6] | 54.0 | 77.7 | 89.7 | 48.6 | 68.9 | 80.2
PCB [49] | 54.6 | 78.4 | - | 54.3 | 72.4 | -
SSG [13] | 58.3 | 80.0 | 90.0 | 53.4 | 73.0 | 80.6
ECN++ [53] | 63.8 | 84.1 | 92.8 | 54.4 | 74.0 | 83.7
MMCL [36] | 60.4 | 84.4 | 92.8 | 51.4 | 72.4 | 82.9
SNR [22] | 61.7 | 82.8 | - | 58.1 | 76.3 | -
AD-Cluster [47] | 68.3 | 86.7 | 94.4 | 54.1 | 72.6 | 82.5
MMT [14] | 71.2 | 87.7 | 94.9 | 65.1 | 78.0 | 88.8
DG-Net++ [54] | 61.7 | 82.1 | 90.2 | 63.8 | 78.9 | 87.8
MEB-Net [48] | 76.0 | 89.9 | 96.0 | 66.1 | 79.6 | 88.3
Ours | 82.2 | 92.7 | 96.9 | 68.3 | 80.9 | 90.2

Table 2: Comparisons with state-of-the-art methods on Dukemtmc-reID (Duke) and
Market-1501 (Market) for domain adaptive tasks.

###  4.2 Comparisons with State-of-the-arts

Unsupervised person re-identification. To verify effectiveness of the proposed
method, we compare our method with the state-of-the-art methods by training
the re-ID model without any labeled data. Table 1 shows the experimental
results on three benchmarks, including Market-1501 (Market), DukeMTMC-reID
(Duke), and MSMT17. On Market-1501, under the same setting, our method
achieves the best performance among the compared methods with mAP=77.7% and
top-1=90.4%, which exceeds the state-of-the-art method (MMT [14]) by large
margins (3.0% for mAP and 2.0% for top-1). On DukeMTMC-reID, we also achieve
evident 3.7% mAP improvement over the current best approach. For a larger and
more challenging dataset (MSMT17), the proposed method has also achieved
comparable performance in terms of mAP and CMC. With the obtained results, we
validate that our method is able to learn richer representations of person
images compared with previous methods.

Unsupervised domain adaptation person re-identification. To further
demonstrate its effectiveness, our method is tested on several domain
adaptation tasks, which are the representative target tasks to validate the
effectiveness of unsupervised representation. These tasks contains Duke-to-
Market and Market-to-Duke. We initialize the backbone with the model pre-
trained on labeled source domain, and fine-tune on unlabeled target domain.

Table 2 summarizes the experimental results on two adaptation tasks. On
Market-to-Duke, compared to the state-of-the-art method, our method obtains
6.2% and 2.8% improvements on mAP and Top-1 accuracy, respectively. Similar
results can be observed in Market-to-Duke. The above impressive performances
demonstrate the necessity and effectiveness of our proposed method for
unsupervised domain adaptation re-ID. More importantly, current advanced
techniques (MMT [14] and MEB-Net [48]) train multiple networks to enhance the
discrimination capability of re-ID model, while our method only use a network
and thus is not required for the additional learning parameters in training
stage compared to MMT and MEB-Net.

###  4.3 Ablation Studies

In this section, we conduct extensive experiments on DukeMTMC-reID to analyze
the effect of our method under different setting.

Applied stage | mAP | top-1 | top-5 | top-10
---|---|---|---|---
$stage\\_\\{0\\}$ | 63.0 | 77.7 | 87.3 | 90.6
$stage\\_\\{1,3\\}$ | 63.3 | 77.8 | 87.5 | 90.7
$stage\\_\\{0,1,2\\}$ | 64.5 | 78.1 | 87.8 | 90.9
$stage\\_\\{0,1,2,3,4\\}$ | 61.8 | 75.9 | 85.8 | 89.8
$stage\\_\\{2,3,4\\}$ | 59.3 | 75.3 | 84.9 | 88.3
$stage\\_\\{2,4\\}$ | 58.4 | 74.7 | 84.7 | 87.7
$stage\\_\\{3,4\\}$ | 57.7 | 74.3 | 84.2 | 87.2
N/A | 54.2 | 72.0 | 81.3 | 85.1
Table 3: Effects in performance upon the choice of the feature maps to employ DDL. $stage\\_\\{0,1,2\\}$ refers that DDL is plugged into network before the intermediate stages: $stage$_0, $stage$_1, and $stage$_2, and others are similar. N/A indicates that DDL outputs the raw input feature map instead of applying DDL.  Applied stage |  $(\alpha,\beta)$ |  mAP |  top-1 |  top-5
---|---|---|---|---
$stage\\_\\{0,1,2\\}$ |  (0.2,0.1) |  56.0 |  73.0 |  82.9
(0.3,0.2) |  60.6 |  75.9 |  86.0
(0.4,0.3) |  64.5 |  78.1 |  87.8
(0.5,0.4) |  56.4 |  71.2 |  83.1
$stage$_{3,4} |  (0.2,0.1) |  55.4 |  72.4 |  82.4
(0.3,0.2) |  56.8 |  73.9 |  84.0
(0.4,0.3) |  57.7 |  74.3 |  84.2
(0.5,0.4) |  55.8 |  73.0 |  83.3
Table 4: Accuracies according to the size $(\alpha,\beta)$ of the erased
region. Upper: Accuracies with DDL deactivated. Middle: Accuracies when DDL
applied for lower-level layers. lower: Accuracies when DDL applied for higher-
level layers.

Dynamic dropblock layer. First, we explore the influence of DDL at different
stages by integrating DDL into ResNet-50. During the training phase, we plug
DDL into network before the intermediate stages: stage 0, stage 1, stage 2,
stage 3, and stage 4. For instance, stage 0 means that DDL is plugged into
network before input map is fed into network. Table 3 reports the experimental
results. From these results, it can be observed that the best accuracy can be
achieved when the DDL is plugged in lower-level layers. However, the
performance of DDL becomes worse under higher-level layers. The reason behind
is that first-layer features are typically more general (_e.g._ , unrelated to
a particular task) while last-layer features exhibit greater levels of
specificity (_e.g._ , semantic and appearance diversity). This indicates that
DDL tends to work if the features are general, meaning suitable to both
pretext task and re-ID task. In addition, as the last line of Table 3, the
performance of our method (w/o DDL) is worse than that of our method, which
demonstrates that our method benefit from the single visual consistency of the
same image’s different views.

Next, we investigate the effect of erased area on accuracy by changing the
value of $\alpha$ and $\beta$. The upper part of Table 4 summaries the
experimental results. From these results, we obtain the best performance with
$\alpha=0.4$ and $\beta=0.3$ for lower-level layers. Meanwhile, when the
erased area is greatly reduced (_i.e_ , $\alpha=0.2$ and $\beta=0.1$), 8.5%
mAP and 5.1% top-1 decreases are observed. This is because the model hard
capture the different semantic information of the object. Furthermore, given
that higher-level feature is semantic and appearance diversity, we also reduce
the size of erased area when DDL is plugged in high-level layers. The results
are reported in the lower part of Table 4, while the findings are still the
same as before.

Method | $\mathcal{L}_{ce}$ | $\mathcal{L}_{st}$ | DDL | $\mathcal{L}_{co}$ |  mAP |  top-1
---|---|---|---|---|---|---
Baseline |  ✓ |  |  |  |  53.0 |  71.6
✓ |  ✓ |  |  |  55.7 |  73.6
Clustering |  ✓ |  |  |  |  50.3 |  69.3
✓ |  ✓ |  |  |  54.0 |  73.4
Baseline+ConsLearn |  ✓ |  ✓ |  ✓ |  ✓ |  63.2 |  77.4
Clustering+ConsLearn |  ✓ |  ✓ |  ✓ |  ✓ |  64.5 |  78.1
Table 5: Ablation studies of our proposed method on the unsupervised person
re-ID task with DukeMTMC-reID. Unlike the baseline, our clustering generates
pseudo labels according to features from a view of the same images and use the
same labels for two encoded views to optimize model.

Clustering. We set the model as the baseline when training the network using
only the traditional classification component. To verify the effectiveness of
our clustering, we conduct extensive ablation studies. From experimental
results in the upper part of Table 5, it can be easily observed that the
performance of our clustering is worse than that of the baseline model. The
main reason is that the clustering (w/o ConsLearn) focus on local detailed
feature and thus the learned features from different data flows contains
different information, leading to different clustering results. However, our
clustering share the same pseudo label for them. When consistency learning is
also conducted (the lower part of Table 5), our clustering is greatly improved
and surpass the baseline model with ConsLearn (Baseline+ConsLearn) by margins
(mAP=1.3% and top-1=0.7%). This indicates that our clustering begins to
ignores low-level features and focus on semantic information with the help of
ConsLearn. In this way, our clustering further enhance the visual consistency
by using the same cluster for two encoded features. Additionally, the softmax-
triplet loss also improve the performance of our clustering method, which
demonstrates the effectiveness of softmax-triplet loss in our proposed
framework.

Figure 3:  Evaluation results of representations with different projection
heads and various dimensions. Figure 4: The dropmask and heatmap at different
layers of ResNet50 [19] over two encoded features (_i.e._ , $v_{1}$ and
$v_{2}$).

Pretext task. As showed in Table 5, the baseline model results in much lower
performance than our final method. A key reason is that the baseline model
mainly focuses on the local detailed features to optimize the target task of
re-ID while discarding meaningful semantic feature. As a pretext or base task,
consistency learning is integrated into the baseline model, abbreviated as
“Baseline + ConsLearn”. Under this setting, the baseline obtain the obvious
improvement (mAP=11.5% and top-1=6.5%), which further demonstrates the
importance of adopting LearnCons as pretext task.

Method |  Parameter |  mAP |  top-1 |  top-5
---|---|---|---|---
Ours (w/ M) |  $(\theta^{(t)})$ |  64.5 |  78.1 |  87.8
$(\theta^{(t-1)})$ |  63.7 |  78.4 |  87.6
$(\theta^{(t-1)},\theta^{(t)})$ |  63.9 |  77.6 |  87.7
$(\theta^{(t-2)},\theta^{(t-1)},\theta^{(t)})$ |  63.9 |  78.2 |  87.7
Ours (w/o M) |  N/A |  59.6 |  74.6 |  85.2
Table 6: Evaluation results of the temporally averaging model under different
update mechanism. M: the temporal averaging model.
$(\theta^{(t-1)},\theta^{(t)})$: the averaging model is updated as
$\theta_{e}^{(t)}=\zeta\theta_{e}^{(t-1)}+\frac{1-\zeta}{2}\theta^{(t-1)}+\frac{1-\zeta}{2}\theta^{(t)}$,
and others are similar.

Momentum update mechanism. The momentum-based averaging model allows us to
enforce the temporal consistency over network model at different iterations
without additional model parameter. To investigate its effectiveness, we
conduct ablation studies by directly use one network s current-iteration
output as feature distribution, which is abbreviated as “Ours (w/o M)”. As
shown in Table 6, “Ours (w/ M)” consistently improves the results over “Ours
(w/o M)”, which represents our method without temporal consistency based on
averaging model. The qualitative results demonstrates that the necessary of
temporal consistency and the single consistency obtained by model at different
stages has different effects on our results.

Projection head. We then study the importance of the linear projections,
_i.e._ , $f(\cdot)$ and $g(\cdot)$. Figure 3 reports evaluation results using
three different architectures for the projection heads: (1) identity mapping;
(2) linear projection; (3) shared linear projection for $f(\cdot)$ and
$g(\cdot)$. We observe that two kinds of linear projection are obviously
better than no projection (_i.e._ , identity mapping), especially for the
large output dimensions (_e.g._ , 2048 in this paper). This indicate that, by
using two projections, more information can be formed and maintained in
$u_{i}$ and $\tilde{u}_{i}$, $i$=1,2. Furthermore, the performance of shared
linear becomes better as output dimensions increases and outperform normal
linear with large dimensions (_e.g._ , 1024 and 2048 in this paper), which
demonstrates that shared linear projection can obtain more information that is
useful for two tasks.

###  4.4 Discussions

From the perspective of representation learning, our method integrate
disparate visual percepts into persistent entities and underlie visual
reasoning in feature space. As shown in the left of Figure 4, the network
model (w/ DDL) no longer pays attention to some semantic parts at early stages
when they are occluded. However, at later stages, the predicted target regions
of training image are backtraced to the initial information by using the
information of the other view regardless of losing the target on a view.
Finally, the heatmap extracted from model almost highlight the entire region
of the person. This indicates that the ability of model to predict the missing
patch of feature maps are enhanced, inching the two different features toward
consistency. On the contrary, the right of Figure 4 shows the model (w/o DDL)
only focus on the most discriminative part of object, not the entire object.
In addition, unlike the pervious clustering, our clustering treats the same
pseudo labels as ground truth labels for two encoded views to bootstrap the
discriminative ability of network model.

Our proposed framework is trained with both unsupervised clustering and
representation learning. In this way, our method can separate the images into
semantic clusters, where images within the same cluster belong to the semantic
classes and images in different clusters are semantically dissimilar. Since
self-supervised representation learning is task-agnostic during training, they
often have to focus on the feature information, which is unrelated to the
current target task. Indeed, in our method, the clustering induces network
model to capture feature representation that concentrates on the target task
of re-ID. Meanwhile, consistency learning encourages clustering to obtain
semantically meaningful features rather than relying on low-level features.

##  5 Conclusion

In this paper, we present a new approach for unsupervised person re-
identification (re-ID) by combining clustering and consistency learning. As a
target task, clustering learning trains a deep neural network using generated
pseudo-labels and concentrates on current task of re-ID. As a pretext task,
consistency learning requires semantic understanding and induces clustering to
obtain semantically meaningful features rather than depending on low-level
features. Experimental results show that our method achieves significant
improvements over a variety of unsupervised or domain adaptive re-ID tasks.

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