# Disentanglement-based Cross-Domain Feature Augmentation for Effective
Unsupervised Domain Adaptive Person Re-identification

Zhizheng Zhang1 Cuiling Lan2 Wenjun Zeng2 Quanzeng You2 Zicheng Liu2
Kecheng Zheng1 Zhibo Chen1 This work was done when Zhizheng and Kecheng were
interns at Microsoft Research Asia. 1University of Science and Technology of
China 2Microsoft Research
{zhizheng<EMAIL_ADDRESS><EMAIL_ADDRESS>
{culan, wezeng, quanzeng.you<EMAIL_ADDRESS>
###### Abstract

Unsupervised domain adaptive (UDA) person re-identification (ReID) aims to
transfer the knowledge from the labeled source domain to the unlabeled target
domain for person matching. One challenge is how to generate target domain
samples with reliable labels for training. To address this problem, we propose
a Disentanglement-based Cross-Domain Feature Augmentation (DCDFA) strategy,
where the augmented features characterize well the target and source domain
data distributions while inheriting reliable identity labels. Particularly, we
disentangle each sample feature into a robust domain-invariant/shared feature
and a domain-specific feature, and perform cross-domain feature recomposition
to enhance the diversity of samples used in the training, with the constraints
of cross-domain ReID loss and domain classification loss. Each recomposed
feature, obtained based on the domain-invariant feature (which enables a
reliable inheritance of identity) and an enhancement from a domain specific
feature (which enables the approximation of real distributions), is thus an
“ideal” augmentation. Extensive experimental results demonstrate the
effectiveness of our method, which achieves the state-of-the-art performance.

##  1 Introduction

Person re-identification (ReID) aims to identify the same person across
different locations, time instances, and cameras. This technique is
potentially useful for many applications such as tracking people for smart
retail and finding missing children. In real-world applications, when a
trained model is deployed to a new environment, it may suffer from severe
performance drop due to the domain gap between the data from the new
environment and the model’s training data. Collecting and manually annotating
data from new environments to fine-tune the model can alleviate this problem
but is costly. A much cheaper and more attractive solution is to employ an
unsupervised domain adaptive technique, which attempts to exploit the
knowledge from labeled source domain and unlabeled target domain to achieve
better performance in the new environment.

Figure 1: Illustration of domain-shared/invariant and domain-specific
components. We represent the ID feature of each person in a scalable way with
domain-shared feature as base and domain-specific feature as enhancement.

Recently, many approaches are proposed for unsupervised domain adaptive person
ReID. Clustering-based approaches [8, 42, 9, 11, 39, 18, 43] are popular which
assign pseudo labels for the target domain samples by applying different
clustering methods. Their common pipeline is to train a model on labeled
source domain and then fine-tune the model on the target domain with pseudo
labels. However, because of the existing domain gap between source and target
datasets, the psuedo-labels obtained/assigned through clustering usually
contain noisy/unreliable labels. Such unreliable labels would mislead the
feature learning and prevent the achievement of high domain adaptation
performance. The lack of reliable labels for the target domain samples is one
big challenge for UDA person ReID. Some methods tend to alleviate the
influence of noisy pseudo labels by exploiting mutual learning [11, 41, 43]
or/and ignoring the outliers [43, 12] for training. For example, NRMT [43]
maintains two networks to perform collaborative clustering and mutual instance
selection, which reduces the chance of fitting to noisy instances by using the
mutual supervision and the reliable instance selection in training. In
contrast, we address the problem from a new perspective, i.e., by generating
target domain samples with reliable labels for training.

Unlike previous methods that generate samples based on complicated generative
models, i.e., Generative Adversarial Networks [34, 6, 23, 14], we propose a
Disentanglement-based Cross-Domain Feature Augmentation (DCDFA) strategy to
generate “ideal” augmented features, which characterize well the target and
source domain data distributions while inheriting reliable identity labels.
Particularly, we disentangle each extracted sample feature into a robust
domain-invariant/shared feature and a domain-specific feature (see Figure 1).
As illustrated in Figure 2, we then perform feature recomposition for a
domain-invariant/shared feature in one domain and a domain-specific feature in
the other domain, to enhance the diversity of sample features used in the
training. We encourage the disentanglement and enable the supervision on the
recomposed features with the constraints of cross-domain ReID loss and domain
classification loss. Such recomposed features act as ideal augmentation, which
enables reliable inheritance of identity (thanks to the use of domain-
invariant features as base) and approximates the real distributions (thanks to
the use of domain-specific features of real samples as enhancement).

Note that our method is very different from previous disentanglement-based UDA
methods [17, 24, 21, 35, 1, 10], which aim to learn domain-invariant features
with reduced feature distribution discrepancy between the source and target
domains. We intend to achieve “ideal” feature augmentation for effective
robust/generalizable feature learning based on disentanglement, where both
domain-invariant feature and domain-specific feature are made full use of.

We summarize our main contributions as follows:

  * •

We address the challenge of lack of reliable identity labels for the target
domain samples in UDA from the perspective of generating diverse target domain
samples which approximate the real data distribution well and have reliabel
labels.

  * •

We propose an effective Disentanglement-based Cross-Domain Feature
Augmentation (DCDFA) strategy, which is capable of providing diverse “ideal”
augmented features for training. Particularly, we disentangle a feature into a
domain-invariant feature and a domain specific feature, then perform cross-
domain recomposition to generate augmented features for training. The
recomposed features not only enable reliable inheritance of identity but also
approximate the real distributions.

Our proposed DCDFA brings significant improvements, and helps to achieve
state-of-the-art performance on top of a strong baseline.

##  2 Related work

###  2.1 Unsupervised Domain Adaptive Person ReID

In recent years, many deep-learning based approaches are designed for
unsupervised domain adaptative person ReID. They can be grouped into three
categories.

Image-style transfer based methods transfer source domain labeled images to
match the styles of target domain images, which can be used to fine-tune the
models [34, 6, 23, 14]. The performance of these approaches is usually limited
by the quality of the translated images, which is still not satisfactory.
Besides, they usually leverage GAN networks, e.g., CycleGAN [47] for the
translation, which increases the training complexity. In our work, we do not
need to transfer the images. Instead, based on recomposition of disentangled
features, we obtain augmented features of source and target domains with
_reliable_ identity labels for training.

Clustering-based pseudo labeling methods are popular and have achieved
impressive performance [8, 42, 9, 39, 11, 41, 18, 43]. They usually pre-train
the model using labeled source samples for learning good feature
representations. To fine-tune the network using target samples, they predict
their pseudo labels based on clustering results using the extracted features.
The performance of these methods is hindered by the noises in pseudo labels
caused by the feature extractor affected by domain gaps and the clustering
itself. To alleviate the influence of noisy/unreliable pseudo labels, mutual
learning among several collaborative peer networks [11, 41] is proposed to
refine pseudo labels with each other. Collaborative clustering and mutual
instance selection [43] is introduced to alleviate the effects of noise.

Our proposed DCDFA method is orthogonal with such clustering-based approaches.
Instead of focusing on refining pseudo labels as in [11, 43], we generate
target domain sample features with reliable labels for training, based on
disentangled features. Our network is simple in design, which does not need
two or more networks as in mutual learning. We demonstrate the effectiveness
of our DCDFA on top of clustering-based methods.

Learning domain-invariant feature based methods intend to learn domain-
invariant features by using adversarial learning [17, 24] or explicitly
reducing the feature distribution discrepancy between source and target domain
measured by some metrics, e.g., Maximum Mean Discrepancy (MMD) [21]. This idea
is widely explored/used in UDA classification [21, 35, 1, 10], which can be
considered as disentanglement-based approaches in a broad sense. However,
domain-invariant features in general cannot include all the discriminative
information, whereas there is still discriminative information in the domain-
specific feature but under-explored. Unlike [21, 17, 24] which aim to learn
domain-invariant features and use only them in inference, we leverage feature
disentanglement to facilitate effective feature augmentation for learning
generalizable/robust features, where both domain-invariant feature and domain-
specific feature are made full use of.

###  2.2 Data Augmentation

Data augmentation aims to to increase the effective size of training data,
where region-level [7, 45] and image-level [28, 4] augmentation are widely
used. Region-level augmentation, e.g., cutout [7] and random erasing [45],
modifies local rectangular regions of the input images to generate partially
occluded data samples for training. Image-level augmentation exploits the
invariance properties of images by applying transformation on the images, such
as rotation, flipping, color jittering while preserving the labels. However,
the augmentations are usually manually designed and heuristically chosen,
where there is no guarantee that they are beneficial and expand the data space
properly. AutoAugment [4] applies reinforcement learning to search optimal
compositions of transformations, where the computational burden is heavy even.
Its variants RandAugment [5], Fast AutoAugment [20] try to alleviate this.
Some methods exploit GANs and VAEs for data augmentation, with the cost of
increasing the complexity of the designs [28]. In this work, we propose a
cross-domain feature augmentation strategy to enable the generation of
“realistic” features for effective UDA person ReID, which enables the
inheritance of reliable identity labels while preserving the target and source
domain data distributions.

Figure 2: Illustration of proposed Disentanglement-based Cross-Domain Feature
Augmentation (DCDFA) for UDA person ReID, which is capable of providing
diverse “ideal” augmented features that have reliable identity labels and
approximate the real data distributions well. For a source image $i$ and a
target image $j$, we obtain the feature $\mathbf{f}_{i}^{s}$ and
$\mathbf{f}_{j}^{t}$. Particularly, we decompose them into domain-shared base
features $\mathbf{b}_{i}$, $\mathbf{b}_{j}$ and domain-specific features
$\mathbf{e}_{i}^{s}$, $\mathbf{e}_{j}^{t}$. We exchange their domain-shared
features to get recomposed features $\mathbf{r}_{j}^{s}$ and
$\mathbf{r}_{i}^{t}$ as augmented results for training, with the constraints
of cross-domain ReID loss and domain classification loss. In inference, for
the target image $j$, feature $\mathbf{f}_{j}^{t}$ is used for matching, where
the attention module can be discarded during testing since
$\mathbf{f}_{j}^{t}=\mathbf{b}_{j}+\mathbf{e}_{j}^{t}$.

##  3 Proposed method

Unsupervised Domain Adaptive (UDA) person ReID aims to adapt the model trained
on a labeled source domain
$\mathcal{D}_{s}=\\{(\mathbf{x}_{i}^{s},{y}_{i}^{s})|_{i=1}^{N_{s}}\\}$ to an
unlabelled target domain
$\mathcal{D}_{t}=\\{\mathbf{x}_{i}^{t}|_{i=1}^{N_{t}}\\}$, where $N_{s}$ and
${N_{t}}$ denote the numbers of samples in $\mathcal{D}_{s}$ and
$\mathcal{D}_{t}$, respectively. In general, there is no overlap of identity
labels between $\mathcal{D}_{s}$ and $\mathcal{D}_{t}$.

For UDA person ReID, there are usually style differences between domains,
resulting in domain gaps. Such domain gaps could be caused by the difference
in environments (e.g., lighting, background, seasons, etc.), and capturing
cameras (e.g., resolution, contrast, hue, saturation, etc.). For images of
different domains, some information is domain transferable (e.g., person
attributes, such as age, gender, clothing color, etc.) while some is domain-
specific (e.g., clothing styles, illumination, etc.). Both of them may contain
discriminative information for ReID.

Considering the lack of reliable labels for target domain samples in training,
we propose an effective Disentanglement-based Cross-Domain Feature
Augmentation (DCDFA) strategy, which provides diverse and “ideal” augmented
features for training. Particularly, as illustrated in Figure 2, we
disentangle each sample feature into a domain-invariant feature and a domain-
specific feature, then perform cross-domain recomposition to generate
augmented features for training. To achieve this, the cross-domain ReID loss
and domain classification loss are designed. The recomposed features act as
“ideal” augmentation, which not only enables reliable inheritance of identity
(thanks to the use of domain-invariant features as base) but also approximates
the real target distributions (thanks to the use of domain-specific features
of real samples as enhancement).

In the following, we first describe the feature disentanglement in Section 3.1
and the cross-domain feature recomposition for augmentation in Section 3.2,
respectively. We elaborate on the optimization designs which drive the network
to disentangle features and perform feature augmentation in Section 3.3.

###  3.1 Feature disentanglement

As illustrated in Figure 2, given a person image $\mathbf{x}$, we extract a
feature map $F\\!\in\\!\mathbb{R}^{C\times H\times W}$ with channel number of
$C$ and spatial resolution of $H\times W$, by using a Convolutional Neural
Network (CNN) (e.g., ResNet-50) as the backbone. A global spatial average
pooling operation is adopted to obtain a feature vector
$\mathbf{f}\\!\in\\!\mathbb{R}^{C}$ from $F$ (i.e., $\mathbf{f}={\rm
pool_{sa}}(F)$) as the feature representation for matching.

We aim to augment the features based on disentangled features for effective
UDA. _For UDA person ReID, an “ideal” augmentation needs to meet two
requirements: 1) the augmented target (or source) domain features should
approximate the real distribution of the target (or source) domain samples; 2)
the augmented features should be supplemented with reliable identity
information so as to facilitate the learning process._

One may wonder what characteristics of the disentanglement is desired to
facilitate such “ideal” augmentation. Motivated by the two above requirements,
we expect to disentangle the feature $\mathbf{f}$ into a domain-
shared/invariant identity base feature $\mathbf{b}$ and a domain-specific
enhancement feature $\mathbf{e}$ as:

$\mathbf{f}=\mathbf{b}+\mathbf{e},$ (1)

where the domain-shared feature $\mathbf{b}$, as identity base feature,
predominates in identifying/recognizing the person identity; the domain-
specific $\mathbf{e}$, as its name implies, constitutes the domain-specific
information and acts as enhancement to the former. The domain-shared features,
together with the domain-specific features of the target (or source) domain,
characterize the data distribution of the target (or source) domain, where the
domain-shared features of different domains are exchangeable between domains
without any destruction to each domain’s distribution.

Particularly, we implement the feature decomposition (_i.e_. disentanglement)
by simply using a channel-wise attention module $\mathbf{m}(\cdot)$ [16, 36]
as:

$\mathbf{b}=\mathbf{m}(F)\odot\mathbf{f},\quad\mathbf{e}=(1-\mathbf{m}(F))\odot\mathbf{f},$
(2)

where $\odot$ denotes element-wise multiplication and
$\mathbf{m}(\cdot)\in\mathbb{R}^{C}$ denotes the response of the channel-wise
attention module. We borrow the design of channel-wise attention module from
CBAM [36]:

$\displaystyle\mathbf{m}(F)=$ $\displaystyle\;\sigma\big{(}W_{1}(W_{0}({\rm
pool}_{sa}(F)))$ (3) $\displaystyle+W_{1}(W_{0}({\rm pool}_{sm}(F)))\big{)},$

where $\sigma(\cdot)$ denotes the sigmoid function,
$W_{0}\\!\in\\!\mathbb{R}^{C/r\times C}$ and
$W_{1}\\!\in\\!\mathbb{R}^{C/r\times C}$, $r$ is a pre-defined positive
integer controlling the ratio of dimension reduction. An activation function
ReLU($\cdot$) is adopted after the first fully connected layer $W_{0}$, and we
omit it to simplify the notation. ${\rm pool}_{sa}(\cdot)$ and ${\rm
pool}_{sm}(\cdot)$ denote spatial average-pooling and spatial max-pooling
respectively.

###  3.2 Cross-domain feature augmentation

As shown in Figure 2, for a person image $i$ from the source domain
$\mathcal{D}_{s}$ and a person image $j$ from the target domain
$\mathcal{D}_{t}$, we first extract their feature vectors $\mathbf{f}_{i}^{s}$
and $\mathbf{f}_{j}^{t}$. Following Eq. (1) and Eq. (2), we then decompose
each of them into a domain-shared identity base feature $\mathbf{b}$ and a
domain-specific enhancement feature $\mathbf{e}$:

$\mathbf{f}_{i}^{s}=\mathbf{b}_{i}+\mathbf{e}_{i}^{s},\quad\mathbf{f}_{j}^{t}=\mathbf{b}_{j}+\mathbf{e}_{j}^{t}.$
(4)

$\mathbf{b}_{i}$ and $\mathbf{b}_{j}$ are expected to encode domain-shared
identity information as the base for images $i$ and $j$, respectively.
Meanwhile, $\mathbf{e}_{i}^{s}$ and $\mathbf{e}_{j}^{t}$ (superscript $s$ and
$t$ denote source and target domain respectively) are expected to encode
domain-specific information as the enhancement.

As discussed in Section 3.1, the domain-shared features of different domains
are exchangeable without damaging the feature distribution for each domain.
Therefore, based on the disentangled features, we recompose them by exchanging
the domain-shared features across domains to have augmented instances in the
feature space:

$\mathbf{r}_{i}^{t}=\mathbf{b}_{i}+\mathbf{e}_{j}^{t},\quad\mathbf{r}_{j}^{s}=\mathbf{b}_{j}+\mathbf{e}_{i}^{s}.$
(5)

Note that for a recomposed feature $\mathbf{r}_{i}^{t}$ or
$\mathbf{r}_{j}^{s}$, its identity information is expected to be inherited
from the domain-shared identity base feature while the domain information is
expected to be inherited from the domain-specific enhancement feature.

###  3.3 Optimization of DCDFA

In the above two subsections, we discussed the characteristics of the
desired/ideal disentanglement and the augmentation. There are two necessary
conditions when ideal disentanglement is achieved. 1) The identity of a
recomposed (or original) feature is determined by the identity of the domain-
shared feature; 2) The domain of a recomposed (or original) feature is
determined by the domain of the domain-specific feature.

We drive the feature disentanglement and augmentation towards our desired
roles by encouraging the recomposed and the original features to meet the
above two conditions in the training process. Generally, for two images of two
persons from different domains, as illustrated in Figure 2, we have two
original features $\mathbf{f}_{i}^{s},\mathbf{f}_{j}^{t}$ and two recomposed
features $\mathbf{r}_{j}^{s},\mathbf{r}_{i}^{t}$, constituting a set
$\\{\mathbf{f}_{i}^{s},\mathbf{f}_{j}^{t},\mathbf{r}_{j}^{s},\mathbf{r}_{i}^{t}\\}$.
Whenever the features $\mathbf{f}_{i}^{s},\mathbf{f}_{j}^{t}$ are well/ideally
disentangled, they should meet the above two necessary conditions. For the
original features and recomposed features (i.e.,
$\\{\mathbf{f}_{i}^{s},\mathbf{f}_{j}^{t},\mathbf{r}_{j}^{s},\mathbf{r}_{i}^{t}\\}$),
the subscript denotes the identity of the person and the superscript denotes
the domain identity. Therefore, for the recomposed features and original
features, their domain labels and identity labels, which are inherited from
domain-specific features and domain-invariant features respectively when we
assume the disentanglement is ideal, could be used as supervision to drive the
learning towards the ideal disentanglement. Particularly, we propose two loss
constraints, i.e., cross-domain person ReID, and domain classification.

Cross-domain person ReID constraint. For each element in the set
$\\{\mathbf{f}_{i}^{s},\mathbf{f}_{j}^{t},\mathbf{r}_{j}^{s},\mathbf{r}_{i}^{t}\\}$,
when we take it as an anchor, there are one positive and two negative samples
within this set based on their associated person identities. We propose a
cross-domain ReID loss $\mathcal{L}_{CID}$ by taking each element in this set
as the anchor in turn to pull features of the same identity and push features
of different identities, which is formulated as:

$\mathcal{L}_{CID}\\!=\\!\frac{1}{4}\sum\limits_{m=1}^{4}\\!\\!\bigg{(}\\!\log\\!\big{(}1\\!+\\!{\rm{exp}}(-s^{+}_{m}/\tau)\sum\limits_{n=1}^{N^{-}_{n}}{\rm{exp}}(s^{-}_{mn}/\tau)\big{)}\bigg{)},$
(6)

where $m$ indexes the elements in
$\\{\mathbf{f}_{i}^{s},\mathbf{f}_{j}^{t},\mathbf{r}_{j}^{s},\mathbf{r}_{i}^{t}\\}$.
With the $m$-th element as the anchor in this set, there are one positive pair
and $N^{-}_{n}\\!=\\!2$ negative pairs. $s^{+}_{m}$ denotes the (cosine)
similarity of the corresponding positive pair, and $s^{-}_{mn}$ denotes the
$n$-th negative pair of that. $\tau$ denotes a trainable temperature value
initialized with one.

Domain classification constraint. As described above, the domain labels as
supervision for
$\\{\mathbf{f}_{i}^{s},\mathbf{r}_{j}^{s},\mathbf{f}_{j}^{t},\mathbf{r}_{i}^{t}\\}$
would be assigned as source (i.e., 1), source (i.e., 1), target (i.e., 0),
target (i.e., 0), respectively. To drive the optimization towards our expected
disentanglement, we use a cross entropy based domain classification loss on
the original features and recomposed features as:

$\displaystyle\mathcal{L}_{Domain}=-\frac{1}{4}$
$\displaystyle\big{(}\log(p(\mathbf{f}_{i}^{s}))+\log(p(\mathbf{r}_{j}^{s}))$
(7)
$\displaystyle+\log(1-p(\mathbf{f}_{j}^{t}))+\log(1-p(\mathbf{r}_{i}^{t}))\big{)},$

where $p(\cdot)$ denotes the probability of being classified as source domain
with a trained domain classifier. We construct the domain classifier by simply
stacking two “FC+ReLU+Dropout” blocks, a FC layer (with 2-dimensional output),
and a softmax function sequentially, in which each “FC+ReLU+Dropout” block
reduces the channel dimension of feature with a ratio of 8 and the dropout
ratio is set to 0.1 experimentally.

The $\mathcal{L}_{CID}$ and $\mathcal{L}_{Domain}$ constrain each other in the
optimization to resist trivial solutions for both re-identification and domain
classification. For example, the joint optimization can avoid solution that
$\mathbf{e}_{i}^{s}\\!=\\!0$ and/or $\mathbf{e}_{j}^{t}\\!=\\!0$. This is
because whenever they are zeros, this prevents the optimization/reduction of
the domain classification loss due to the conflict: $\mathbf{b}_{i}$ is
assigned with “source” label (left top $\mathbf{f}_{i}^{s}$ in Figure 2)
whenever it is combined with $\mathbf{e}_{i}^{s}$, while $\mathbf{b}_{i}$ is
assigned with “target” label (right bottom $\mathbf{r}_{i}^{t}$) whenever it
is combined with $\mathbf{e}_{j}^{t}$. The jointing learning of
$\mathcal{L}_{CID}$ and $\mathcal{L}_{Domain}$ drives $\mathbf{b}_{i}$ and
$\mathbf{b}_{j}$ to learn domain-shared base feature, and drives
$\mathbf{e}_{i}^{s}$ and $\mathbf{e}_{j}^{t}$ to learn domain-specific
enhancement feature.

In order to capture as much discriminative information as possible for the
feature before decomposition (i.e., $\mathbf{f}$) and keep consistent with the
original loss designs in our baseline scheme, we add the same basic ReID loss
(e.g., triplet loss and identity loss) to the feature before decomposition,
which we denote it as $\mathcal{L}_{BReID}$. On top of the clustering-based
baseline, we experimentally find that adding ReID loss (triplet loss and
identity loss) on the learned identity base feature, _i.e_. $\mathbf{b}_{j}$,
denoted by $\mathcal{L}_{RReID}^{b}$, improves the performance, which may play
a role of regularization by explicitly encouraging identity base feature to be
discriminative for ReID.

During testing, for a target image $j$, the feature
$\mathbf{f}_{j}^{t}=\mathbf{b}_{j}+\mathbf{e}_{j}^{t}$, which consists of
domain-shared base information and domain-specific enhancement information, is
used for person matching. Thus, the decomposition module can be discarded and
we do not introduce any computational increase at inference when compared with
our baseline.

##  4 Experiments

###  4.1 Implementation

We build a strong baseline based on the clustering-based method and
incorporate our proposed DCDFA on top of the strong baseline. We elaborate on
them respectively.

Baseline. We follow the general pipeline of clustering-based UDA person ReID
methods [8, 29, 18, 43] to build our baseline, which consists of three main
stages, i.e., network pre-training, assignment of pseudo labels by clustering,
and network fine-tuning. As illustrated in Figure 3, we first pre-train the
network using source domain labeled data $\mathcal{D}_{s}$, where we add the
basic ReID loss $\mathcal{L}_{BReID}$ (triplet loss and identity loss) on
feature $\mathbf{f}^{s}$. We then perform clustering on the extracted features
of the unlabeled target domain data $\mathcal{D}_{t}$ to generate pseudo
labels for $\mathcal{D}_{t}$. Fine-tuning is performed by adding the basic
ReID loss on $\mathbf{f}^{t}$ supervised with the predicted pseudo labels.
Clustering and fine-tuning are performed iteratively.

Figure 3: Flowchat of our proposed strong baseline SBase for UDA person ReID.
In comparison with the plain clustering-based baseline, it additionally uses
the Mean-Teacher method and a memory bank (for contrastive loss). For the
Mean-Teacher method, it maintains a temporal moving average (i.e.the Mean-Net)
of the student network (bottom network), where the gradient back-propagation
is only performed on the student network. The feature
$\mathbf{f}^{{}^{\prime}}$ from the Mean-Net is used for testing. Note that
our DCDFA will be applied on the extracted feature $\mathbf{f}$.

Strong baseline (SBase). We incorporate the Mean-Teacher method [31] which
maintains a temporal moving average (Mean-Net) of the student (basic) network,
and memory bank mechanism [15], to strengthen the baseline to have a strong
baseline scheme _SBase_ for UDA person ReID. Figure 3 illustrates this
clustering-based strong baseline. In the inference, only the Mean-Net is used
while the student network is discarded. We will present the ablation study of
each component in Section 4.2.

We build a memory bank and update it with the prototypes (i.e.each prototype
is an averaged feature of each person) of $\mathcal{D}_{s}$, and instance
features of $\mathcal{D}_{t}$. In the joint fine-tuning stage, given a query
sample from $\mathcal{D}_{s}$ or $\mathcal{D}_{t}$, we add contrastive loss
(with re-weighting as in circle loss [30]) w.r.t. this query and elements in
the memory bank to enlarge the within-identity similarity and reduce the
cross-identity similarity. Memory bank allows the collection of sufficient
hard negative pairs across more mini-batches for network optimization [33].

Ours (SBase+DCDFA). We validate the effectiveness of our proposed DCDFA method
on top of the strong baseline (SBase). Given a mini-batch consisting of $N$
source images and $N$ target images, for simplicity, we randomly select $N$
source-target pairs where each source image appears in exactly one pair and so
does each target image. For each pair, as illustrated in Figure 2, we add the
proposed two loss constraints for optimization. $N$ is set to 64.

Implementation details. We use ResNet50 pretrained on ImageNet as our backbone
networks. Following the good practices in person ReID [25], we perform widely
used data augmentation of cropping, flipping, and random erasing [45] on the
images in all experiments. For source pre-training, each mini-batch contains
64 images of 4 identities. For our fine-tuning stage using both source and
target data, each mini-batch contains $N$=64 source-domain images of 4
identities and $N$=64 target-domain images of 4 pseudo identities, in which
there are 16 images for each identity. All images are resized to
256$\times$128\. Similar to [11, 38], we use the clustering algorithm of
DBSCAN, where the maximum distance between neighbors is set to $eps=0.6$ and
the minimal number of neighbors for a dense point is set to 4. Adam optimizer
is adopted. The initial learning rate is set to 0.00035. The hyperparameter
$r$ (see around Eq. (3)) is set to 8. To balance the relative importance of
different losses (e.g., $\mathcal{L}_{CID}$, $\mathcal{L}_{Domain}$,
$\mathcal{L}_{BReID}$), following [2], we set the balancing weights to make
the gradients of different loss items lie in a similar range and found weights
of 1 work well. More details can be found in the Supplementary.

Datasets and evaluation metrics. We conduct experiments using three popular
person ReID datasets: DukeMTMC-reID [27] (abbreviated as “Duke” ), Market-1501
[44] (abbreviated as “Market” ), and MSMT17 [34]. DukeMTMC-reID [27] has
36,411 images, where 702 identities are used for training and 702 identities
for testing. Market-1501 [44] contains 12,936 images of 751 identities for
training and 19,281 images of 750 identities for testing.MSMT17 [34] contains
126,441 images, where 1,041 and 3,060 identities are used for training and
testing respectively. We use the commonly used settings, i.e., Market $\to$
Duke, Duke $\to$ Market, Market $\to$ MSMT17, Duke $\to$ MSMT17 for
evaluation. We adopt mean average precision (mAP) and CMC Rank-1/5/10
(R1/R5/R10) accuracy (%) for evaluation.

###  4.2 Ablation study

On top of a strong baseline (_SBase_), we validate the effectiveness of our
DCDFA and also study the effectiveness of each technical component in _SBase_.

####  4.2.1 Effectiveness of components in strong baseline

We adopt two technologies, i.e., Mean-Teacher method (Mean-Net), memory bank
(MB), to improve the plain baseline of UDA person ReID to be a strong
baseline, which is denoted by “SBase”. Table 1 shows the comparisons. 1)
Memory bank (MB) brings improvement of 1.7%/1.7% and 3.9%/0.8% in mAP/Rank-1
on Market$\to$Duke and Duke$\to$Market, respectively. 2) Mean-Teacher
component (Mean-Net) produces 3.0%/3.2% and 4.0%/0.7% improvements in
mAP/Rank-1 on Market$\to$Duke and Duke$\to$Market, respectively. 3) Our strong
baseline _SBase_ applies both components and delivers better results.

Methods |  Market$\to$Duke |  Duke$\to$Market
---|---|---
mAP |  R1 |  mAP |  R1
Supervised learning |  72.4 |  86.0 |  83.0 |  94.2
Baseline (Base) |  60.4 |  75.9 |  68.2 |  87.9
Base+MB |  62.1 |  77.6 |  72.1 |  88.7
Base+Mean-Net |  63.4 |  79.1 |  72.2 |  88.6
Base+MB+Mean-Net (SBase) |  64.8 |  79.7 |  75.4 |  89.8
Table 1: Ablation study of components in our clustering-based strong baseline. Here, “MB” denotes memory bank and Mean-Net denotes the Mean-Teacher method. “SBase” refers to our strong baseline with both “MB” and Mean-Net. Methods |  Market$\to$Duke |  Duke$\to$Market
---|---|---
mAP |  R1 |  mAP |  R1
SBase |  64.8 |  79.7 |  75.4 |  89.8
SBase+DCDFA (using $\mathbf{b}_{j}$) |  63.9 |  78.9 |  75.1 |  89.3
SBase+DCDFA |  68.9 |  82.2 |  78.6 |  91.5
Table 2: Effectiveness of our proposed DCDFA on top of _SBase_.

####  4.2.2 Effectiveness of our proposed DCDFA

As shown in Table 2, compared with the strong baseline _SBase_ , thanks to our
disentanglement-based feature augmentation, our scheme _SBase+DCDFA_ achieves
4.1% and 3.2% improvements in terms of mAP on the two settings
(Market$\to$Duke and Duke$\to$Market), which demonstrates the effectiveness of
our DCDFA.

One may wonder how about the performance of our scheme when we use the domain-
shared base feature alone, i.e., $\mathbf{b}_{j}$, for inference, rather then
using $\mathbf{f}_{j}^{t}$, which constitutes of domain-shared base feature
and domain-specific enhancement feature. Table 2 shows that _SBase+DCDFA_
significantly outperforms _SBase+DCDFA_ (using $\mathbf{b}_{j}$) by 5.0% and
3.5% in mAP on Market$\to$Duke and Duke$\to$Market, respectively. This
indicates the domain-specific enhancement features also contain helpful
discriminative information for ReID. Our joint exploration of them by
generating “ideal” augmentation is an effective strategy for improving
performance of UDA. Moreover, we found using only domain-shared feature is
even poorer than the baseline _SBase_. That may be because some identity
discriminative information is lost when using only domain-shared features and
thus this brings performance loss, even though the domain gap is alleviated in
the domain-shared feature. On top of our strong baseline _SBase_ , we also
implemented the classical adversarial domain adaption method [10] which aims
to learn domain shared/invariant features for adaptation. Similarly, the
performance is close to that of _SBase_ , which outperforms _SBase_ only by
0.1%/0.5% in mAP on Market$\to$Duke/Duke$\to$Market.

Methods |  Market$\to$Duke |  Duke$\to$Market
---|---|---
mAP |  R1 |  mAP |  R1
SBase |  64.8 |  79.7 |  75.4 |  89.8 [t]
SBase+$\mathcal{L}_{CID}$ |  66.1 |  80.2 |  76.0 |  89.9
SBase+$\mathcal{L}_{CID}$+$\mathcal{L}_{RReID}^{b}$ |  66.6 |  81.4 |  76.3 |  91.0
SBase+$\mathcal{L}_{CID}$+$\mathcal{L}_{Domain}$ |  67.5 |  81.7 |  77.2 |  91.1
SBase+$\mathcal{L}_{CID}$+$\mathcal{L}_{Domain}$+$\mathcal{L}_{RReID}^{b}$ |  68.9 |  82.2 |  78.6 |  91.5

Table 3: Ablation study for loss function designs in our proposed DCDFA.
$\mathcal{L}_{CID}$ and $\mathcal{L}_{Domain}$ denote the proposed cross-
domain ReID loss and domain classification loss for original features and
recomposed features across domains, respectively. $\mathcal{L}_{RReID}^{b}$
denotes the basic ReID loss (triplet loss and identity loss) added on learned
identity base feature $\mathbf{b}_{j}$ for regularization.

Methods |  Market1501$\to$DukeMTMC |  DukeMTMC$\to$Market1501
---|---|---
mAP |  R1 |  R5 |  R10 |  mAP |  R1 |  R5 |  R10
MMFA [21] |  BMVC’18 |  24.7 |  45.3 |  59.8 |  66.3 |  27.4 |  56.7 |  75.0 |  81.8
ATNet [23] |  CVPR’19 |  24.9 |  45.1 |  59.5 |  64.2 |  25.6 |  55.7 |  73.2 |  79.4
SPGAN+LMP [6] |  CVPR’18 |  26.2 |  46.4 |  62.3 |  68.0 |  26.7 |  57.7 |  75.8 |  82.4
CFSM [3] |  AAAI’19 |  27.3 |  49.8 |  - |  - |  28.3 |  61.2 |  - |  -
BUC [22] |  AAAI’19 |  27.5 |  47.4 |  62.6 |  68.4 |  38.3 |  66.2 |  79.6 |  84.5
ECN [46] |  CVPR’19 |  40.4 |  63.3 |  75.8 |  80.4 |  43.0 |  75.1 |  87.6 |  91.6
UCDA [26] |  ICCV’19 |  31.0 |  47.7 |  - |  - |  30.9 |  60.4 |  - |  -
PDA-Net [19] |  ICCV’19 |  45.1 |  63.2 |  77.0 |  82.5 |  47.6 |  75.2 |  86.3 |  90.2
PCB-PAST [42] |  ICCV’19 |  54.3 |  72.4 |  - |  - |  54.6 |  78.4 |  - |  -
SSG [38] |  ICCV’19 |  53.4 |  73.0 |  80.6 |  83.2 |  58.3 |  80.0 |  90.0 |  92.4
ACT [37] |  AAAI’20 |  54.5 |  72.4 |  - |  - |  60.6 |  80.5 |  - |  -
MPLP [32] |  CVPR’20 |  51.4 |  72.4 |  82.9 |  85.0 |  60.4 |  84.4 |  92.8 |  95.0
DAAM [17] |  AAAI’20 |  48.8 |  71.3 |  82.4 |  86.3 |  53.1 |  77.8 |  89.9 |  93.7
AD-Cluster [40] |  CVPR’20 |  54.1 |  72.6 |  82.5 |  85.5 |  68.3 |  86.7 |  94.4 |  96.5
DIM+GLO [24] |  MM’20 |  58.3 |  76.2 |  85.7 |  88.5 |  65.1 |  88.3 |  94.7 |  96.3
MMT [11] |  ICLR’20 |  65.1 |  78.0 |  88.8 |  92.5 |  71.2 |  87.7 |  94.9 |  96.9
NRMT [43] |  ECCV’20 |  62.2 |  77.8 |  86.9 |  89.5 |  71.7 |  87.8 |  94.6 |  96.5
B-SNR+GDS-H [18] |  ECCV’20 |  59.7 |  76.7 |  - |  - |  72.5 |  89.3 |  - |  -
MEB-Net [41] |  ECCV’20 |  66.1 |  79.6 |  88.3 |  92.2 |  76.0 |  89.9 |  96.0 |  97.5
SpCL [13] |  arXiv’20 |  68.8 |  82.9 |  90.1 |  92.5 |  76.7 |  90.3 |  96.2 |  97.7
SBase+DCDFA |  Ours |  68.9 |  82.2 |  91.2 |  93.0 |  78.6 |  91.5 |  96.5 |  97.7

Methods |  Marke1501$\to$MSMT17 |  DukeMTMC$\to$MSMT17
---|---|---
mAP |  R1 |  R5 |  R10 |  mAP |  R1 |  R5 |  R10
ECN [46] |  CVPR’19 |  8.5 |  25.3 |  36.3 |  42.1 |  10.2 |  30.2 |  41.5 |  46.8
SSG [38] |  ICCV’19 |  13.2 |  31.6 |  - |  49.6 |  13.3 |  32.2 |  - |  51.2
DAAM [17] |  AAAI’20 |  20.8 |  44.5 |  - |  - |  21.6 |  46.7 |  - |  -
NRMT [43] |  ECCV’20 |  19.8 |  43.7 |  56.5 |  62.2 |  20.6 |  45.2 |  57.8 |  63.3
MMT [11] |  ICLR’20 |  22.9 |  49.2 |  63.1 |  68.8 |  23.3 |  50.1 |  63.9 |  69.8
SpCL [12] |  NeurIPS’20 |  26.8 |  53.7 |  65.0 |  69.8 |  - |  - |  - |  -
SBase+DCDFA |  Ours |  25.9 |  52.8 |  65.3 |  70.1 |  27.1 |  54.3 |  67.8 |  70.7

Table 4: Performance (%) comparison with the state-of-the-art methods for
Unsupervised Domain Adaptation (UDA) person ReID on DukeMTMC-reID, Market-1501
and MSMT17 datasets. We mark the second-best results by underline and the best
results by bold text.

####  4.2.3 Ablation study for proposed loss designs

We conduct ablation studies to evaluate the effectiveness of our proposed loss
functions for optimizing DCDFA. Table 3 shows the results. We observe that
adopting $\mathcal{L}_{CID}$ only on top of SBase improves the performance
slightly, while jointly using both $\mathcal{L}_{CID}$ and
$\mathcal{L}_{Domain}$ significantly improves SBase by 2.7% and 1.8% in mAP on
Market$\to$Duke and Duke$\to$Market, respectively. $\mathcal{L}_{CID}$ tends
to derive domain-shared identity information into $\mathbf{b}_{j}$ but there
still lacks an explicit constraint to push domain-specific information into
$\mathbf{e}_{j}^{t}$. Adding $\mathcal{L}_{Domain}$ helps to promote
$\mathbf{e}_{i}^{s}$ and $\mathbf{e}_{j}^{t}$ to contain domain-specific
information. They jointly drive the achievement of desired feature
disentanglement.

In addition, we find that adding the ReID loss (triplet loss and identity
loss) i.e., $\mathcal{L}_{RReID}^{b}$, on the learned identity base feature,
_i.e_. $\mathbf{b}_{j}$, further improves the performance. It plays a role of
regularization which makes optimization easier by explicitly encouraging
identity base feature to be discriminative for ReID.

####  4.2.4 Ablation study for attention choices

We use an attention module to decompose/disentangle features and find the
channel-wise attention is most effective. Please see our Supplementary for
more analysis and results.

###  4.3 Complexity

In testing, since our decomposition/attention module can be discarded, we do
not introduce any computational increase at inference when compared with our
baseline. In training, the introduction of our DCDFA only slightly increases
the time complexity (i.e., less than 5%).

###  4.4 Comparison with the state-of-the-arts

We compare our proposed _SBase+DCDFA_ with the state-of-the-art methods on
four domain adaptation settings in Table 4. Our _SBase+DCDFA_ achieves the
best performance when compared with the state-of-the-art methods on
Market$\to$Duke and Duke$\to$Market in mAP accuracy. On Market$\to$MSMT, our
mAP accuracy is lower than SpCL [12] by 0.9%. SpCL introduces a self-paced
method which gradually creates more reliable clusters to refine the hybrid
memory and learning targets. Our DCDFA, as a feature augmentation strategy, is
conceptually complementary to it. We believe that applying the DCDFA on top of
SpCL would further improve its performance. SSG [38] performs multiple
clustering on both global body and local body parts. DAAM [17] learns domain-
invariant features for person ReID. As discussed before, using domain-
invariant feature alone is inadequate since some discriminative information is
domain-specific. MMT [11] uses two networks (four models) and MEB-Net [41]
employs three networks (six models) to perform mutual mean teacher training,
suffering high computation complexity in training. Our DCDFA uses only one
network (two models) in training but still significantly outperforms MEB-Net
[41].

###  4.5 Extension to cross-quality UDA person ReID

We investigated the effectiveness of our proposed DCDFA on cross-quality UDA
person ReID, where the person image qualities are different between the source
and target domains. See our Supplementary for more details.

##  5 Conclusion

In this work, we propose a Disentanglement-based Cross-Domain Feature
Augmentation (DCDFA) strategy to generate “ideal” augmented features for
training, where the augmented features characterize well the target and source
domain data distributions while inheriting reliable identity labels.
Particularly, we disentangle each sample feature into a domain-shared feature
and a domain-specific feature, and perform cross-domain feature recomposition
to increase the diversity of samples used in training. Thanks to our designs,
the recomposed features act as “ideal” augmentation, which enables reliable
inheritance of identity and approximiates the real distributions. Experiments
demonstrate the effectiveness of our DCDFA. As a feature augmentation
strategy, DCDFA is generic and could be used on top of the existing UDA
methods to further enhance their performance.

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