# Source Data-Free Cross-Domain Semantic Segmentation: Align, Teach and
Propagate

Yuxi Wang, Jian Liang, , and Zhaoxiang Zhang  Y. Wang, J. Liang, and Z. Zhang
are with the Center for Research on Intelligent Perception and Computing
(CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of
Automation, Chinese Academy of Sciences (CASIA), and the University of Chinese
Academy of Sciences (UCAS), Beijing 100190, China. E-mail:
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>Zhang
is the corresponding author.

###### Abstract

Benefiting from considerable pixel-level annotations collected from a specific
situation (source), the trained semantic segmentation model performs quite
well but fails in a new situation (target). To mitigate the domain gap,
previous cross-domain semantic segmentation methods always assume the co-
existence of source data and target data during domain alignment. However,
accessing source data in the real scenario may raise privacy concerns and
violate intellectual property. To tackle this problem, we focus on an
interesting and challenging cross-domain semantic segmentation task where only
the trained source model is provided to the target domain. Specifically, we
propose a unified framework called ATP, which consists of three schemes, i.e.,
feature Alignment, bidirectional Teaching, and information Propagation. First,
considering explicit alignment is infeasible due to no source data, we devise
a curriculum-style entropy minimization objective to implicitly align the
target features with unseen source features via the provided source model.
Second, besides positive pseudo labels in vanilla self-training, we introduce
negative pseudo labels to this field and develop a bidirectional self-training
strategy to enhance the representation learning in the target domain. It is
the first work to use negative pseudo labels during self-training for domain
adaptation. Finally, the information propagation scheme is employed to further
reduce the intra-domain discrepancy within the target domain via pseudo-semi-
supervised learning, which is the first step by providing a simple and
effective post-process for the domain adaptation field. Furthermore, we also
extend the proposed to the more challenging black-box source-model scenario
where only the source model’s prediction is available. Extensive results on
synthesis-to-real and cross-city driving datasets validate ATP yields state-
of-the-art performance, even on par with methods that need access to source
data.

###### Index Terms:

Domain adaptation, source data-free, feature alignment, negative pseudo
labeling, information propagation

##  1 Introduction

Semantic segmentation is a fundamental task in the computer vision field that
aims to estimate the pixel-level predictions for a given image. Despite the
rapid progress on deep learning-based methods, attaining high performance
usually demands vast amounts of training data with pixel-level annotations.
However, collecting large-scale datasets tends to be prohibitively expensive
and time-consuming. Alternatively, previous cross-domain learning approaches
[58, 90, 63, 95, 110, 49, 117, 89] attempt to train a satisfactory
segmentation model for unlabeled real-world data (called target domain) by
exploiting labeled photo-realistic synthetic images (called source domain).
However, due to the domain discrepancy, a well-performing model trained on the
source domain degrades drastically when it is applied to the target domain. To
tackle this issue, various domain alignment strategies are proposed in domain
adaptive semantic segmentation field [34, 90, 7, 32, 52, 84, 50].

The essential idea for domain adaptive semantic segmentation is to effectively
transfer knowledge from a labeled source domain to a distinct unlabeled target
domain. Previous methods achieve this goal by bridging the domain gap in the
image level [9, 13, 32, 110, 42], feature level [5, 34, 7], and output level
[90, 63, 95, 91]. Although these approaches have made remarkable progress,
they usually require access to the labeled source data during the alignment
process. However, in some crucial scenarios, the source data is inaccessible
due to data protection laws. For instance, the source datasets of autonomous
driving and face recognition are unavailable due to data privacy. Thus, this
paper addresses a challenging and interesting source data-free domain
adaptation issue, where only the trained source model is provided to the
target domain for adaptation instead of the source data.

Figure 1: Comparison between the conventional, source data-free, and black-box
source-model domain adaptation for semantic segmentation. The proposed source
data-free domain adaptation method only relies on the trained source model,
while the conventional methods require all the labeled source data and the
target data. Moreover, the black-box model where only the source model’s
predictions are available can also be leveraged for adaptation.

Training a source data-free adaptation model is more challenging than
conventional unsupervised domain adaptation approaches. As Figure 1 shows, we
describe the difference between traditional unsupervised domain adaptation and
source data-free domain adaptation. Due to the absence of source data,
previous feature-level alignment or pixel-level alignment methods are not
feasible anymore. Moreover, pseudo labels generated from the source-only model
are unreliable because of the performance degradation. Besides, we also extend
our approach to the black-box source model scenario, where only the
predictions of the source model are available, as shown in Figure 1 (c). This
problem becomes more challenging but more flexible and practical in the rule
of the sever-and-client paradigm. Recent works attempt to address source data-
free domain adaptive semantic segmentation by model adaptation [81, 36] or
estimating the source data [57]. The approaches highly rely on the prior
knowledge of the source model and partially ignore the target-specific
information, leading to sub-optimal solutions. Moreover, we are the first to
attempt to solve the black-box source model domain adaptive semantic
segmentation in this field.

In this paper, we propose a new framework called ATP to tackle source data-
free domain adaptive semantic segmentation, which focuses on exploiting the
target knowledge to enhance feature representation. Specifically, three
pivotal factors are introduced to guide the procedure: implicit feature
alignment, bidirectional self-training, and information propagation. Due to
the absence of source data, explicit feature alignment approaches are
infeasible for source data-free domain adaptation. Consequently, we first
provide the implicit feature alignment scheme that aims to ensure the target
features are aligned with the source features through hypothesis transfer
[53]. Then, considering hard samples perform poorly due to domain discrepancy,
explicitly emphasizing these samples at the early training stage may lead to
the convergence issue. To address this, we incorporate the idea of Curriculum
Learning (CL) [118, 97, 18, 43, 23, 77] into domain adaptation. We develop a
curriculum-style entropy loss to emphasize easy samples first and hard samples
later automatically. During the bidirectional self-training process, we are
among the first to introduce negative pseudo-labeling to the domain adaptation
field. Specifically, the negative pseudo labels refer to the predictions with
low confidence scores, providing reliable supervised information to indicate
the absent classes for corresponding pixels. We further consider vanilla
class-balanced [124, 123] positive pseudo labels with high confidence scores
and develop a bidirectional self-training strategy to enhance the target
representation learning. Finally, the information propagation scheme aims to
reduce intra-domain discrepancy within the target domain because of the
different transferability of the target data. Particularly, we conduct a proxy
semi-supervised learning task for target data and integrate dominant semi-
supervised learning techniques to boost the performance. This propagation acts
as a generic simple and effective post-processing module for domain
adaptation. Furthermore, we also extend a challenging black-box source model
domain adaptation scenario, and the proposed method is spontaneously suitable
for this.

The main contributions of this work are summarized as follows.

  1. 1.

We propose a novel source data-free domain adaptation method for semantic
segmentation that only requires a trained source model and unlabeled target
data. The proposed method can also perform well on the black-box source model
scenario, where only the source model’s predictions are available.

  2. 2.

We develop a novel curriculum-style entropy objective for implicitly aligning
the features of source and target domains.

  3. 3.

We propose a new bidirectional self-training strategy that considers both the
positive pseudo labeling and negative pseudo labeling.

  4. 4.

The proposed method yields state-of-the-art cross-domain results on Cityscapes
[15] with the semantic segmentation mIoU by 52.6% and 57.9% when adapting from
GTA5 [79] and SYNTHIA [80]. Moreover, it performs on par with domain
adaptation methods with accessing the source data.

The rest of this paper is organized as follows. Section II gives a brief
review for related works about unsupervised domain adaptation, domain adaptive
semantic segmentation, source data-free domain adaptation, and semi-supervised
learning. Section III presents the detailed procedure of the proposed ATP
scheme, including implicit feature alignment, bidirectional self-training, and
information propagation. Section IV shows the details, results, analysis, and
ablation studies of adaptation experiments in GTA5$\rightarrow$Cityscapes,
SYNTHIA$\rightarrow$Cityscapes, and Cityscapes$\rightarrow$Cross-City [10]
tasks. Section V concludes the paper and provides the consideration of the
future work.

##  2 Related Work

###  2.1 Unsupervised Domain Adaptation

As a typical field of transfer learning [74], unsupervised domain adaptation
(UDA) aims to transfer the knowledge from the labeled source domain to a
related but distinct unlabeled target domain. Early UDA methods [114, 88, 4]
assume the covariate shift with the identical conditional distributions across
domains and reduce the domain discrepancy by estimating the weight of each
source instance and re-weighting the source empirical risk. Later, researchers
[25, 73, 51] resort to domain-invariant feature learning where a common space
with the aligned distributions is learned. However, the transferability of
these shallow methods is restricted due to the limited representation ability
[59].

Recently, deep neural networks approaches have achieved inspiring results for
unsupervised domain adaptation in many visual applications, for instance,
image classification [96, 119, 98, 61, 27], face recognition [39, 29],
objection detection [28, 108, 8, 94], person re-identification [105, 19, 21,
66], and semantic segmentation [90, 63, 49, 72, 95, 99, 116]. The structures
of these methods usually have three components: deep feature extractor, task-
specific classifier, and domain-invariant feature learning module. Based on
the strategies of domain-invariant feature learning, existing deep UDA can be
roughly divided into three distinct categories: discrepancy-based,
adversarial-based, and reconstruction-based. Discrepancy-based approaches aim
to minimize the distance between the source and target distributions by
utilizing a divergence criterion. Some favoring choices include maximum mean
discrepancy (MMD) [60], high-order central moment discrepancy [115],
contrastive domain discrepancy [40], and the Wasserstein metric [20]. As great
success has been achieved by GAN method [26], adversarial-based has been a
mainstream UDA approach that learns domain-invariant feature representations
via confusing source and target domains. DANN [22] adopts a gradient reversal
layer (GRL) as a domain-invariant feature learning module, ensuring the
feature distributions over two domains are made similar. ADDA [92] provides a
symmetrical architecture for source and target mapping, which is flexible to
learn a powerful feature extractor. Besides, in contrast to the above binary
classifier methods, following works attempt to align joint distribution by
considering multiple class-wise domain classifiers [76] or a semantic multi-
output classifier [14, 46]. Moreover, reconstruction-based approaches [24]
utilize an auxiliary reconstruction task to create a shared domain-invariant
representation between two domains and keep the individual characteristic of
each domain. In addition, some other reconstruction-based methods [3, 69]
further improve the adaptation performance by seeking domain-specific
reconstruction and cycle consistency. Beyond them, some other studies also
investigate batch normalization [64, 98] and adversarial dropout [83, 47]
techniques to ensure feature alignment.

###  2.2 Domain Adaptive Semantic Segmentation

Domain adaptive semantic segmentation (DASS) is a challenging application for
UDA, which aims to provide pixel-level predictions for unlabeled target data.
Since Hoffman, et al. [33] introduced DASS, it has achieved much attention due
to annotating pixel-level labels is labor-expensive and time-consuming.
Existing domain adaptation methods for semantic segmentation can be roughly
categorized into two groups: adversarial learning based methods and self-
supervised learning based methods. For adversarial learning, numerous works
focus on reducing the domain discrepancy in the image level, feature level,
and output level. Specifically, image-level based methods aim to transfer the
domain “style” (appearance) from target to source [106], from source to target
[13, 99, 107, 101, 75], or consider both [32, 122, 9, 11, 49, 117]. Feature-
level based approaches align distributions between the source and target data
at different layers of networks by minimizing the feature discrepancy [120] or
adversarial training via a domain classifier [33, 35, 37]. Output-level
adversarial training is first proposed by [90], introducing a discriminator to
distinguish the predictions obtained from the source or the target domain.
Besides, [95] derives so-called “weighted self-information maps” for
minimizing domain discrepancy and [72] diminishes the domain gap by
introducing an intra-domain adversarial training process.

For the self-supervised learning method, the essential idea is to generate
reliable pseudo labels. Typical approaches usually consist of two steps: 1)
generate pseudo labels based on the source model [124, 123, 65] or the learned
domain-invariant model [121, 50, 87], 2) refine the target model supervised by
the generated pseudo labels [116, 99]. Moreover, [82] and [89] provide a new
unaligned score to measure the efficiency of a learned model on a new target
domain. Although these methods have achieved promising results, they usually
depend heavily on the labeled source data during adaptation. In this paper, we
address a challenging source data-free domain adaptation without any raw
source data due to the data privacy policy.

###  2.3 Source Data-free Domain Adaptation

Source data-free domain adaptation is introduced by Chidlovskii et al. [12],
to tackle domain adaptation problems without accessing the original source
dataset. To tackle this problem, [48] exploits the pretrained source model to
generate target-style samples, and [53, 1] learns a target-specific feature
extraction module by implicitly aligning target representations to the source
hypothesis. [45] proposes a universal source data-free domain adaptation when
the knowledge of the label space in the target domain is not available.
Moreover, a few recent approaches [102, 109, 104] provide model adaptation
solutions for classification problems. As for semantic segmentation tasks,
model adaptation is also a feasible method to tackle the source data absent
setting. Specifically, [57] leverages a generative model to synthesize fake
samples to estimate the source distribution and preserve source domain
knowledge via knowledge transfer during model adaptation. [81] proposes an
uncertainty-reducing method to enhance feature representation. [44] and [111]
achieve promising performance via generating a generalized source model
trained by data augmentation strategies. In contrast, we offer a simple and
effective framework for semantic segmentation tasks, exploiting the target-
specific knowledge to intensify the target feature learning. In this paper, we
propose a standard three-step framework for domain adaptive semantic
segmentation without source data, which includes implicit feature alignment,
bidirectional self-training, and information propagation.

Figure 2: Illustration of the proposed align, teach, and propagate (ATP)
framework for domain adaptive semantic segmentation without source data. (a)
is the implicit feature alignment. We freeze the classifier and train the
feature extractor to achieve implicit feature alignment by minimizing
$\mathcal{L}_{cel}$ and $\mathcal{L}_{div}$. (b) illustrates the process of
bidirectional self-training. Positive and negative pseudo labels are obtained
from a fixed model initialized from stage (a). (c) illustrates the process of
information propagation. The target data is divided into easy and hard splits
according to the entropy ranking, and then we reduce intra-domain discrepancy
in a semi-supervised manner by minimizing loss $\mathcal{L}_{ssl}$.

###  2.4 Semi-supervised Semantic Segmentation

Semi-supervised learning (SSL) methods exploit a small number of labeled data
and a large amount of unlabeled data during training. The key for semi-
supervised learning is to learn a consistent representation between the
labeled and unlabeled data. To achieve this goal, recent approaches can be
divided into two groups, adversarial-based and consistency-based. Inspired by
GAN [26], adversarial-based approaches [38, 67, 86] conduct adversarial loss
between labeled and unlabeled data. Consistency-based methods [62, 71, 112]
utilize data augmentation and consistency regularization to learn feature
representation for unlabeled data. Furthermore, [68] first introduces Gaussian
perturbations into the input that changes output predictions. Then the strong
data augmentation is adopted as solid perturbations. [93] uses the Mixup as
the consistency regularization. [103] uses auto-augmentation to generate
augmented inputs, and [2] extends the idea by dividing the augmentation set
into strong and weak operations. In this work, we refer to the semi-supervised
learning strategy to domain adaptation, aiming to reduce the intra-domain
discrepancy within the target domain.

##  3 The Proposed Method

In this section, we first introduce the proposed ATP for source data-free
domain adaptation, which includes three schemes: the implicit feature
alignment, bidirectional self-training, and information propagation. Then we
extend the proposed method to a more challenging black-box source model case,
where only source model’s predictions are available.

During training, we are given the source model
$\mathcal{M}_{s}:\mathcal{X}_{s}\rightarrow\mathcal{Y}_{s}$ trained on $n_{s}$
labeled images $\\{x_{s}^{i},y_{s}^{i}\\}_{i=1}^{n_{s}}$ from the source
domain $\mathcal{D}_{s}$ and $n_{t}$ unlabeled images
$\\{x_{t}^{i}\\}_{i=1}^{n_{t}}$ from the target domain $\mathcal{D}_{t}$,
where $x_{s}^{i}\in\mathcal{X}_{s}$, $y_{s}^{i}\in\mathcal{Y}_{s}$, and
$x_{t}\in\mathcal{X}_{t}$. Our goal is to learn a segmentation mapping
$\mathcal{M}_{t}:\mathcal{X}_{t}\rightarrow\mathcal{Y}_{t}$ transferring from
$\mathcal{M}_{s}$, which predicts a pixel-wise label
$y_{t}^{i}\in\mathcal{Y}_{t}$ for a given image $x_{t}^{i}$.

The semantic segmentation model $\mathcal{M}_{s}$ on the source domain is
obtained in a supervised manner by minimizing the following cross-entropy
loss,

$\mathcal{L}_{ce}=-\frac{1}{n_{s}}\sum_{i=1}^{n_{s}}\sum_{j=1}^{H\times
W}\sum_{c=1}^{C}y_{s}^{(i,j,c)}\log p_{s}^{(i,j,c)},$ (1)

where $n_{s}$ is the number of source images, $H$ and $W$ denote the image
size, and $C$ denotes the number of categories. $p_{s}^{(i,j,c)}$ denotes the
predicted category probability by $\mathcal{M}_{s}$ and the $y_{s}^{(i,j,c)}$
is the corresponding one-hot ground-truth label. Generally, the segmentation
model $\mathcal{M}_{s}$ consists of a feature extractor $f_{s}$ and a
classifier $g_{s}$, i.e., $\mathcal{M}_{s}=f_{s}\circ g_{s}$.

To transfer the trained $\mathcal{M}_{s}$ to the target domain, the proposed
ATP framework mainly includes three stages, shown in Figure 2. The implicit
feature alignment implicitly enforces the target feature to fit the source
model via the proposed curriculum-style entropy minimization and weighted
diversity loss, referring to Sec. 3.1. In this stage, the target model is
initialized by $\mathcal{M}_{s}$ and the classifier is frozen during training.
Then, we enhance target feature representation learning by the proposed
bidirectional self-training technique described in Sec. 3.2, including the
positive pseudo labeling and negative pseudo labeling. Finally, we achieve
information propagation in a semi-supervised manner, which reduces the intra-
domain discrepancy and boosts the adaptation performance with a significant
improvement. We will elaborate on the details in the following.

###  3.1 Implicit Feature Alignment

Without source data, explicit feature alignment that directly minimizes the
domain gap between the source and target data can not be implemented. Besides,
self-training techniques perform poorly because pseudo labels generated from
the source-only model are unreliable. To tackle this problem, we resort to
hypothesis transfer and labeling transfer inspired by Liang et al. [53]. In
contrast, we propose a curriculum-style entropy loss for implicit feature
alignment to adapt the source model to the target data. The details will be
described in the following.

To eliminate the domain gap, entropy minimization strategy is adopted for
training. Although previous methods [95, 72] have demonstrated the
effectiveness of entropy minimization, they are not appropriate for the source
data-free setting because they treat equal importance for different samples.
However, hard-to-transfer samples with uncertain predictions (high entropy
values) may deteriorate the target feature learning procedure. To address this
issue, we attempt to explore more reliable supervision from the easy-to-
transfer examples with certain predictions (lower entropy values). Inspired by
the curriculum learning algorithm [124, 118], we focus the target data
training on automatically emphasizing the easy samples first and hard samples
later. Specifically, we exploit a curriculum-style entropy loss that expects
to rapidly focus the model on certain predictions and down-weight the
contribution of uncertain predictions. Formally, the curriculum-style entropy
loss is formulated as follows:

$\mathcal{L}_{cel}=\alpha*(1-h(x_{t}))^{\gamma}*h(x_{t}),$ (2)

where $\alpha$ balances the importance of certainty/uncertainty predictions
and $\gamma$ controls the weight of certainty samples.
$h(x_{t})=-\sum_{c=1}^{C}p_{x_{t}}^{(h,w,c)}\log p_{x_{t}}^{(h,w,c)}$ denotes
the entropy map and $p_{x_{t}}^{(h,w,c)}$ is the predicted probability of the
target image $x_{t}$, i.e., $p_{x_{t}}^{(h,w,c)}=f_{t}\circ g_{t}(x_{t})$.
$f_{t}$ and $g_{t}$ denote the feature extractor and classifier for the target
data. Weights of hard-to-transfer samples with higher $h(x_{t})$ are reduced
and easy samples are emphasized relatively. During training, we pursue
hypothesis transfer [53] by fixing classifier module to implicitly align the
target features with the source features, i.e., $g_{t}=g_{s}$. As we utilize
the same classifier module for different domain-specific features, the optimal
target features are enforced to fit the source feature distribution as they
should have a similar one-hot encoding output. Experiments in Sec. 4 reveal
this strategy is important.

From the above loss, the trained model concentrates more on easy-to-transfer
classes with a sight domain gap or a large pixel proportion, which may lead to
a trivial solution with under-fitting on hard-to-transfer classes. To tackle
this issue, we develop a diversity-promoting loss to ensure the global
diversity of the target outputs. Exactly, we expect to see the prediction of
the target output containing all categories. The proposed confidence-weighted
diversity objective is below,

$\mathcal{L}_{div}=\sum_{c=1}^{C}\hat{p}_{x_{t}}^{(h,w,c)}\log\hat{p}_{x_{t}}^{(h,w,c)},$
(3)

where $\hat{p}_{x_{t}}^{(h,w,c)}$ is the weighted mean output embedding of the
target image $x_{t}$. The weight is calculated based on the entropy $h(x_{t})$
and
$\hat{p}_{x_{t}}^{(h,w,c)}=\sum_{(h,w)}\exp({-\lambda*h(x_{t})})*p_{x_{t}}^{(h,w,c)}$.
$\lambda$ is a hyper-parameter and we empirically set $\lambda=3$ in all
experiments. By diversifying the output of target prediction, this technique
can circumvent the problem of wrongly predicting confusing target instances as
relatively easy classes to learn.

Although the formulation of the proposed curriculum-style entropy loss is
similar to focal loss [55], they are entirely different. Specifically, the
former is inspired by curriculum learning and it focuses the model adaption on
samples from easy to hard. The latter tackles an unbalanced problem by
emphasizing hard samples during training.

###  3.2 Bidirectional Self-training

Previous self-supervised learning methods focus on strengthening the
reliability of pseudo labels by developing various denoising strategies [116,
99], but they ignore most of the pixels with lower prediction confidence
scores. In that case, the model tends to be over-fitting because the selected
over-confident samples lack references using negative pseudo labels. To remedy
this, we restrict our attention to the predictions with lower confidence
values, termed negative pseudo labels. Although low-confidence predictions
cannot be assigned as the correct labels, they will definitely indicate
specific absent classes. As Figure 3 shows, it is hard to clearly indicate
which category it belongs to for the ignore pixels (black areas), but it is
easy to determine which categories it does not belong to. Inspired by this, we
propose a bidirectional self-training method that includes positive pseudo
labeling and negative pseudo labeling.

Positive pseudo labeling. We first introduce the class-balanced positive
pseudo labeling method, which aims to select reliable pseudo labels with high
confidence scores for training. Considering the domain-transfer difficulty
among different categories, generating pseudo labels according to high
confidence will cause hard-to-transfer categories to be ignored. Because easy-
to-transfer classes (e.g., roads, buildings, walls, sky) usually have higher
prediction confidence scores, while hard-to-transfer classes (e.g., lights,
signals, trains, bicycles) perform poorly. Consequently, we assign pseudo-
labels based on the guidance of the category-level threshold inspired by [124]
as below,

$\hat{y}_{x_{t}}^{(h,w,c)}=\begin{cases}1&\mathrm{if}\
c=(\mathrm{argmax}_{c}p_{x_{t}}^{(h,w,c)})\cap(p_{x_{t}}^{(h,w,c)}>\lambda_{c})\\\
0&\mathrm{otherwise},\end{cases}$ (4)

where $\lambda_{c}$ is a category-level threshold that is determined by the
top $K$ predictions within the class $c$.

Then, we optimize the target model by minimize the categorical cross-entropy
with every pseudo label $\hat{y}_{t}$:

$\mathcal{L}_{ppl}=-\frac{1}{n_{t}}\sum_{i=1}^{n_{t}}\sum_{j=1}^{H\times
W}\sum_{c=1}^{C}\hat{y}_{t}^{(i,j,c)}\log p_{t}^{(i,j,c)},$ (5)

where $n_{t}$ is the number of target images, $p_{t}^{(i,j,c)}$ denotes the
predicted category probability, and $\hat{y}_{t}^{(i,j,c)}$ is the
corresponding pseudo label obtained from Eq. (4).

Figure 3: Bidirectional pseudo labels. Blue refers to positive pseudo labels
and red refers to negative pseudo labels.

Negative pseudo labeling. For the negative pseudo-label assignment, our goal
is to explore the implicit knowledge in uncertainty-aware regions that have
been ignored in previous works. Although these predictions have higher
uncertainty due to the uniform or low prediction scores, they can definitely
point out the absent categories. For example, for a pixel whose output is
[0.48, 0.47, 0.02, 0.03], it is impossible to determine which category it
belongs to, but we can confidently point out that it does not belong to
classes with scores of 0.02 and 0.03. Therefore, we use all absent classes as
supervision information for target feature learning. Formally, we assign the
negative pseudo labels as follows:

$\delta(p_{x_{t}}^{(h,w,c)})=\begin{cases}1&\mathrm{if}\
p_{x_{t}}^{(h,w,c)}<\lambda_{neg},\\\ 0&\mathrm{otherwise},\end{cases}$ (6)

where $\lambda_{neg}$ is the negative threshold and we use
$\lambda_{neg}=0.05$ for all experiments. It is worth noticing that the
negative pseudo labels are binary multi-labels, which certainly indicates the
absence of relative classes. Then the negative pseudo labeling objective is
formulated as follows:

$\mathcal{L}_{npl}=-\frac{1}{n_{t}}\sum_{i=1}^{n_{t}}\sum_{j=1}^{H\times
W}\sum_{c=1}^{C}\delta(p_{t}^{(i,j,c)})\log(1-p_{t}^{(i,j,c)}).$ (7)

Finally, the bidirectional self-training objective includes the positive and
negative pseudo labeling:

$\mathcal{L}_{bst}=\mathcal{L}_{ppl}+\mathcal{L}_{npl}.$ (8)

###  3.3 Information Propagation

Even we propose a much better solution for global adaptation without source
data, intra-domain discrepancy still exists, as previous works [72, 57]
indicate. Inspired by the intra-domain alignment method [72], we first divide
the target data into easy and hard splits and then close the intra-domain gap
to boost the adaptation performance. Contrary to the traditional adversarial
mechanism, we borrow a semi-supervised learning scheme by considering the easy
split as labeled data and the hard split unlabeled. The target domain is
separated using the entropy ranking strategy as below,

$r(x_{t})=\frac{1}{HW}\sum_{h=1}^{H}\sum_{w=1}^{W}h(x_{t})^{(h,w)},$ (9)

which is the mean entropy for a target image $x_{t}$ with the corresponding
entropy map $h(x_{t})$. Easy and hard splits are conducted from the ranking of
$r(x_{t})$ following $ratio=\frac{\|x_{te}\|}{(\|x_{te}\|+\|x_{th}\|)}$, where
$x_{te}$ and $x_{th}$ denote the easy split and the hard split, respectively.

After that, we conduct a proxy semi-supervised learning task based on the
assumption that it is necessary to mitigate intra-domain discrepancy and
pseudo labels of the easy part are reliable. We borrow semi-supervised
learning techniques to bridge the intra-domain gap. We incorporate dominant
semi-supervised learning approaches, including consistency-based methods [70,
103] and contrastive learning methods [56], and propose the following
propagation objective:

$\begin{split}\mathcal{L}_{ssl}=&\mathcal{L}_{ce}(\mathcal{M}_{t}(x_{te}),\hat{y}_{te})+\\\
&\mathcal{L}_{cyc}(\mathcal{M}_{t}(\mathrm{\textbf{Aug}}(x_{th})),\mathcal{M}_{t}^{{}^{\prime}}(x_{th})),\end{split}$
(10)

where $\hat{y}_{te}$ denotes the pseudo labels of $x_{te}$ obtained from the
trained model in Eq. (8). $\mathcal{L}_{ce}$ is the cross-entropy loss and
$\mathcal{L}_{cyc}$ is the consistency loss [103]. $\mathrm{\textbf{Aug}}(x)$
denotes data augmentation operation for image $x$.
$\mathcal{M}_{t}^{{}^{\prime}}$ offers the values of target model
$\mathcal{M}_{t}$ without gradient optimization.

Unlike the most closely related work [72] that reduces intra-domain gap by
conducting intra-domain adversarial learning, our work directly learns
consistency representations for the target data in a semi-supervised learning
manner. Notice that the adopted technique can be considered as a standard
post-process for adaptation, which is more simple and stable than the
adversarial learning method.

Data: training dataset: $\mathcal{X}_{t}$; the trained source model:
$\mathcal{M}_{s}$; parameters: $\alpha$, $\gamma$, $\lambda_{c}$, and
$\lambda_{neg}$;

Result: the output target model $\mathcal{M}_{t}$

1

2Warmup: $\mathcal{M}_{s}\leftarrow(\mathcal{X}_{s},\mathcal{Y}_{s})$
according to Eq.(1), which is trained before;

3 Align:

4initialization: $\mathcal{M}_{t_{0}}=f_{t_{0}}\circ
g_{t_{0}}\leftarrow\mathcal{M}_{s}$. $f_{t_{0}}$ is optimized and $g_{t_{0}}$
is frozen.

5for  _$m\leftarrow 0$ to $epochs$_ do

6 Optimize $\mathcal{M}_{t_{0}}$ by minimizing the proposed losses
$\mathcal{L}_{cel}$ Eq. (2) and $\mathcal{L}_{div}$ Eq. (3);

7 end for

8 Teach:

9initialization: $\mathcal{M}_{t_{1}}\leftarrow\mathcal{M}_{t_{0}}$;

10 $\mathcal{P}$ is a set of positive prediction samples; $\mathcal{N}$ is a
set of negative prediction samples;

11 for  _$stage\leftarrow 1$ to $S$_ do

12 Generate positive pseudo labels set
$\mathcal{P}\leftarrow\mathcal{M}_{t_{1}}(\mathcal{X}_{t};\lambda_{c})$
according to Eq. (4);

13 for  _$m\leftarrow 0$ to $epochs$_ do

14 Get negative pseudo labels
$\mathcal{N}\leftarrow\mathit{M}_{t_{1}}(\mathcal{X}_{t};\lambda_{neg})$
according to Eq. (6);

15 Train model $\mathcal{M}_{t_{1}}$ using the proposed bidirectional self-
training technique according to Eq. (8);

16

17 end for

18

19 end for

20

21 Propagate:

22 initialization: $\mathcal{M}_{t}\leftarrow\mathcal{M}_{t_{1}}$;

23 Divide the target data $\mathcal{X}_{t}$ into easy split $\mathcal{X}_{te}$
and hard split $\mathcal{X}_{th}$ according to Eq. (9);

24 Generate pseudo labels $\mathcal{\hat{Y}}_{te}$ for easy split
$\mathcal{X}_{te}$;

25 for  _$m\leftarrow 0$ to $epochs$_ do

26

27 Apply data augmentation for the hard target data Aug($x_{th}$);

28 Optimize $\mathcal{M}_{t}$ by minimizing the proposed loss
$\mathcal{L}_{ssl}$ according to Eq. (10);

29 end for

30

Return $\mathcal{M}_{t}$

Algorithm 1 ATP TABLE I: Results of GTA5 $\rightarrow$ Cityscapes (CS). “SF”
denotes the source data-free setting. Red is the best result for source data-
free methods. Blue is the best for source data-dependent methods.

Method | SF |  road |  side. |  build. |  wall |  fence |  pole |  light |  sign |  vege. |  terr. |  sky |  person |  rider |  car |  truck |  bus |  train |  motor. |  bike |  mIoU
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
AdaptSeg [90] | ✗ | 86.5 | 36.0 | 79.9 | 23.4 | 23.3 | 23.9 | 35.2 | 14.8 | 83.4 | 33.3 | 75.6 | 58.5 | 27.6 | 73.7 | 32.5 | 35.4 | 3.9 | 30.1 | 28.1 | 42.4
ADVENT [95] | 89.4 | 33.1 | 81.0 | 26.6 | 26.8 | 27.2 | 33.5 | 24.7 | 83.9 | 36.7 | 78.8 | 58.7 | 30.5 | 84.8 | 38.5 | 44.5 | 1.7 | 31.6 | 32.4 | 45.5
CBST [124] | 91.8 | 53.5 | 80.5 | 32.7 | 21.0 | 34.0 | 28.9 | 20.4 | 83.9 | 34.2 | 80.9 | 53.1 | 24.0 | 82.7 | 30.3 | 35.9 | 16.0 | 25.9 | 42.8 | 45.9
MaxSquare [7] | 89.4 | 43.0 | 82.1 | 30.5 | 21.3 | 30.3 | 34.7 | 24.0 | 85.3 | 39.4 | 78.2 | 63.0 | 22.9 | 84.6 | 36.4 | 43.0 | 5.5 | 34.7 | 33.5 | 46.4
CAG_UDA [117] | 90.4 | 51.6 | 83.8 | 34.2 | 27.8 | 38.4 | 25.3 | 48.4 | 85.4 | 38.2 | 78.1 | 58.6 | 34.6 | 84.7 | 21.9 | 42.7 | 41.1 | 29.3 | 37.2 | 50.2
FDA [110] | 92.5 | 53.3 | 82.4 | 26.5 | 27.6 | 36.4 | 40.6 | 38.9 | 82.3 | 39.8 | 78.0 | 62.6 | 34.4 | 84.9 | 34.1 | 53.1 | 16.9 | 27.7 | 46.4 | 50.5
PLCA [41] | 84.0 | 30.4 | 82.4 | 35.3 | 24.8 | 32.2 | 36.8 | 24.5 | 85.5 | 37.2 | 78.6 | 66.9 | 32.8 | 85.5 | 40.4 | 48.0 | 8.8 | 29.8 | 41.8 | 47.7
SIM [100] | 90.6 | 44.7 | 84.8 | 34.3 | 28.7 | 31.6 | 35.0 | 37.6 | 84.7 | 43.3 | 85.3 | 57.0 | 31.5 | 83.8 | 42.6 | 48.5 | 1.9 | 30.4 | 39.0 | 49.2
SFDA [57] |  | 84.2 | 39.2 | 82.7 | 27.5 | 22.1 | 25.9 | 31.1 | 21.9 | 82.4 | 30.5 | 85.3 | 58.7 | 22.1 | 80.0 | 33.1 | 31.5 | 3.6 | 27.8 | 30.6 | 43.2
URMA [81] |  | 92.3 | 55.2 | 81.6 | 30.8 | 18.8 | 37.1 | 17.7 | 12.1 | 84.2 | 35.9 | 83.8 | 57.7 | 24.1 | 81.7 | 27.5 | 44.3 | 6.9 | 24.1 | 40.4 | 45.1
S4T [78] | ✓ | 89.7 | 39.2 | 84.4 | 25.7 | 29.0 | 39.5 | 45.1 | 36.8 | 86.8 | 41.8 | 79.3 | 61.2 | 26.7 | 85.0 | 19.3 | 28.2 | 5.3 | 11.8 | 9.3 | 44.8
SFUDA [111] |  | 95.2 | 40.6 | 85.2 | 30.6 | 26.1 | 35.8 | 34.7 | 32.8 | 85.3 | 41.7 | 79.5 | 61.0 | 28.2 | 86.5 | 41.2 | 45.3 | 15.6 | 33.1 | 40.0 | 49.4
HCL [36] |  | 92.0 | 55.0 | 80.4 | 33.5 | 24.6 | 37.1 | 35.1 | 28.8 | 83.0 | 37.6 | 82.3 | 59.4 | 27.6 | 83.6 | 32.3 | 36.6 | 14.1 | 28.7 | 43.0 | 48.1
ATP (w/o TP) |  | 90.1 | 33.7 | 83.7 | 31.5 | 20.6 | 31.7 | 34.5 | 25.4 | 84.8 | 38.2 | 82.0 | 59.9 | 26.9 | 84.5 | 25.9 | 38.9 | 6.9 | 23.8 | 32.1 | 45.0
ATP (w/o P) | ✓ | 90.4 | 43.9 | 85.1 | 40.4 | 23.8 | 34.2 | 41.8 | 34.3 | 84.2 | 35.9 | 85.5 | 62.3 | 21.9 | 83.8 | 36.9 | 51.8 | 0.0 | 36.8 | 53.0 | 49.8
ATP |  | 93.2 | 55.8 | 86.5 | 45.2 | 27.3 | 36.6 | 42.8 | 37.9 | 86.0 | 43.1 | 87.9 | 63.5 | 15.3 | 85.5 | 41.2 | 55.7 | 0.0 | 38.1 | 57.4 | 52.6

###  3.4 Extension for Black-Box Source Model

In this section, we extend our method to the challenging black-box source
model case [54], where only the target data and the corresponding predictions
of the source model are available. It is a more practical scenario because we
can utilize all kinds of server API as our source model. As no parameters are
available, the source model can be treated as a black box, only providing the
predictions of the target data. Due to no access to the trained source model
parameters, the implicit feature alignment is impractical. Alternatively, a
natural knowledge distillation (KD) [31] method is adopted to transfer the
soft predictions from the source model to the target model. In this case, we
pursue the knowledge distill to enforce the target model performing a
similarity prediction with the source model. The knowledge distillation loss
is formulated as follows:

$\mathcal{L}_{kd}=\mathrm{E}_{x_{t}\in\mathcal{X}_{t}}\mathcal{D}_{kl}(f_{s}\circ
g_{s}(x_{t});f_{t}\circ g_{t}(x_{t})),$ (11)

where $\mathcal{D}_{kl}$ denotes the Kullback-Leibler divergence loss. After
that, we apply our bidirectional self-training strategy and information
propagation based on the target student model.

###  3.5 Algorithm

In this section, we provide the algorithm of ATP in Algorithm 1, which
consists of three stages, Align, Teach, and Propagate. The first stage refers
to the implicit feature alignment, which enforces the target feature
representations fitting to the source model. In the second stage, we use
bidirectional self-training strategy that contains the positive pseudo
labeling and the negative pseudo labeling to enhance the feature
representation learning. The third stage is related to the information
propagation, which reduces intra-domain discrepancy in a semi-supervised
learning manner.

TABLE II: Results of SYNTHIA $\rightarrow$ Cityscapes. “SF” denotes the source
data-free setting. Red is the best result for source data-free methods. Blue
is the best result for source data-dependent methods.

Method | SF |  road |  side. |  build. |  wall∗ |  fence∗ |  pole∗ |  light |  sign |  vege. |  sky |  person |  rider |  car |  bus |  motor. |  bike |  mIoU∗ |  mIoU
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
AdaptSeg [90] | ✗ | 79.2 | 37.2 | 78.8 | - | - | - | 9.9 | 10.5 | 78.2 | 80.5 | 53.5 | 19.6 | 67.0 | 29.5 | 21.6 | 31.3 | - | 45.9
ADVENT [95] | 85.6 | 42.2 | 79.7 | 8.7 | 0.4 | 25.9 | 5.4 | 8.1 | 80.4 | 84.1 | 57.9 | 23.8 | 73.3 | 36.4 | 14.2 | 33.0 | 41.2 | 48.0
CBST [124] | 68.0 | 29.9 | 76.3 | 10.8 | 1.4 | 33.9 | 22.8 | 29.5 | 77.6 | 78.3 | 60.6 | 28.3 | 81.6 | 23.5 | 18.8 | 39.8 | 42.6 | 48.9
MaxSquare [7] | 82.9 | 40.7 | 80.3 | 10.2 | 0.8 | 25.8 | 12.8 | 18.2 | 82.5 | 82.2 | 53.1 | 18.0 | 79.0 | 31.4 | 10.4 | 35.6 | 41.4 | 48.2
CAG_UDA [117] | 84.8 | 41.7 | 85.8 | - | - | - | 13.7 | 23.0 | 86.5 | 78.1 | 66.3 | 28.1 | 81.8 | 21.8 | 22.9 | 49.0 | - | 52.6
PLCA [41] | 82.6 | 29.0 | 81.0 | 11.2 | 0.2 | 33.6 | 24.9 | 18.3 | 82.8 | 82.3 | 62.1 | 26.5 | 85.6 | 48.9 | 26.8 | 52.2 | 46.8 | 54.0
SIM [100] | 83.0 | 44.0 | 80.3 | - | - | - | 17.1 | 15.8 | 80.5 | 81.8 | 59.9 | 33.1 | 70.2 | 37.3 | 28.5 | 45.8 | - | 52.1
FDA [110] | 79.3 | 35.0 | 73.2 | - | - | - | 19.9 | 24.0 | 61.7 | 82.6 | 61.4 | 31.1 | 83.9 | 40.8 | 38.4 | 51.1 | - | 52.5
SFDA [57] |  | 81.9 | 44.9 | 81.7 | 4.0 | 0.5 | 26.2 | 3.3 | 10.7 | 86.3 | 89.4 | 37.9 | 13.4 | 80.6 | 25.6 | 9.6 | 31.3 | 39.2 | 45.9
URMA [81] |  | 59.3 | 24.6 | 77.0 | 14.0 | 1.8 | 31.5 | 18.3 | 32.0 | 83.1 | 80.4 | 46.3 | 17.8 | 76.7 | 17.0 | 18.5 | 34.6 | 39.6 | 45.0
S4T [78] | ✓ | 84.9 | 43.2 | 79.5 | 7.2 | 0.3 | 26.3 | 7.8 | 11.7 | 80.7 | 82.4 | 52.4 | 18.7 | 77.4 | 9.6 | 9.5 | 37.9 | 39.3 | 45.8
SFUDA [111] |  | 90.9 | 45.5 | 80.8 | 3.6 | 0.5 | 28.6 | 8.5 | 26.1 | 83.4 | 83.6 | 55.2 | 25.0 | 79.5 | 32.8 | 20.2 | 43.9 | 44.2 | 51.9
HCL [36] |  | 80.9 | 34.9 | 76.7 | 6.6 | 0.2 | 36.1 | 20.1 | 28.2 | 79.1 | 83.1 | 55.6 | 25.6 | 78.8 | 32.7 | 24.1 | 32.7 | 43.5 | 50.2
ATP (w/o TP) |  | 89.7 | 44.6 | 80.1 | 3.3 | 0.3 | 28.4 | 8.1 | 7.7 | 81.1 | 85.2 | 55.2 | 19.1 | 83.4 | 34.3 | 16.7 | 32.4 | 41.8 | 49.0
ATP (w/o P) | ✓ | 89.8 | 45.1 | 82.9 | 0.1 | 0.0 | 33.8 | 14.6 | 17.3 | 84.5 | 87.0 | 59.3 | 25.6 | 85.8 | 49.4 | 26.2 | 56.1 | 46.9 | 55.9
ATP |  | 90.1 | 46.3 | 82.5 | 0.0 | 0.1 | 31.7 | 10.7 | 17.9 | 85.1 | 87.7 | 64.6 | 34.6 | 86.4 | 54.8 | 33.7 | 58.3 | 49.0 | 57.9

##  4 Experiments

###  4.1 Datasets

We demonstrate the efficacy of the proposed method on the standard adaptation
tasks of GTA5 [79]$\rightarrow$Cityscapes [15], SYNTHIA
[80]$\rightarrow$Cityscapes and Cityscapes$\rightarrow$NTHU Cross-City [10].
The synthetic dataset GTA5 contains 24,966 annotated images with a resolution
of 1914$\times$1052, token from the famous game Grand Theft Auto. The ground
truth is generated by the game rendering itself. SYNTHIA is another synthetic
dataset, which contains 9,400 fully annotated images with the resolution of
1280 $\times$ 760\. Cityscapes consists 2,975 annotated training images, and
500 validation images with the resolution of 2048 $\times$ 1024\. NTHU Cross-
City dataset has been recorded in four cities: Rome, Rio, Tokyo, and Taipei.
Each city set has 3,200 unlabeled training images and 100 testing images with
the resolution of 2048 $\times$ 1024\. We employ the mean Intersection over
Union (mIoU) as the evaluation metric which is widely adopted in semantic
segmentation tasks.

###  4.2 Implementation Details

We use the DeepLab [6] architecture with ResNet-101 [30] pre-trained on
ImageNet [16] as the backbone, which is the same as previous works [90, 63,
95]. We first pre-train the segmentation network on the source domain for 80k
iterations to obtain a high-quality source model. Then it is considered as the
initialization model. During training, we follow previous works [90, 63, 95]
using Stochastic Gradient Descent (SGD) optimizer with the learning rate
$2.5\times 10^{-4}$, momentum 0.9, and weight decay $5\times 10^{-4}$. We
schedule the learning rate using “poly” policy: the learning rate is
multiplied by $(1-\frac{iter}{max\\_iter})^{0.9}$. In the entropy minimization
stage, we fix the classifier as above mentioned. In the other stages, the
classifier is optimized with the learning rate $2.5\times 10^{-3}$. Concerning
parameters, we follow unsupervised hyper-parameters selection method [82] to
tune the following parameters. We first freeze $\gamma$ and choose
$\alpha=0.002$ from the range of [0.001, 0.002, 0.003], and then the value of
$\gamma$ is selected as 3 from the range of [0,1,2,3,4,5]. For self-training
process, we assign the threshold $\lambda_{c}$ according to the category-level
top $K=65\%$ target predictions to generate pseudo-labels, and the threshold
of negative pseudo labels $\lambda_{neg}$ is assigned as 0.05. We update
pseudo labels epoch by epoch, and the maximum number of epochs is empirically
set as 10. For the semi-supervised training process, we assign the target
images with average entropy ranked top $50\%$ as the easy group, and the other
images are defined as the hard group. Generally, we randomly run our methods
three times with different random seeds via PyTorch and report the average
accuracy.

###  4.3 Results

In this section, we show the performance of proposed ATP on
GTA5$\rightarrow$Cityscapes in Table I, SYNTHIA$\rightarrow$Cityscapes in
Table II, and Cityscapes$\rightarrow$Cross-City in Table III. ATP (w/o TP),
ATP (w/o P), and ATP denote results of different training stages, namely
implicit feature alignment, bidirectional self-training, and information
propagation, respectively. In addition, we compare our method with previous
state-of-the-art works, including traditional source data-dependent methods
[90, 95, 124, 117, 110, 7, 41, 100] and source data-free methods [57, 81, 78,
111, 36]. The utility of source data can directly align distributions of
source and target domains. Thus conventional source data-dependent approaches
obtain higher performance than source data-free adaptation methods reasonably.
Notice that our method ATP performing domain adaptation without source data
achieves comparable results or even better than source data-dependent methods.
Furthermore, concerning source data-free scenarios, our method outperforms
previous works [57, 81, 36, 78, 111] with a large margin.

Specifically, for the tasks of GTA5$\rightarrow$Cityscapes and
SYNTHIA$\rightarrow$Cityscapes, as shown in Table I and II, our method arrives
at mIoU scores 52.6% and 57.9%, respectively, which offers a large margin
performance gain compared to the non-adaptation baseline. Moreover, the
proposed method surpasses four existing source data-free methods SFDA [57],
URMA [81], SFUDA [111], and HCL [36], with 9.4%, 7.5%, 3.2%, and 4.5% mIoU
scores on GTA5$\rightarrow$Cityscapes, and 12.0%, 12.9%, 6.0%, and 7.7% mIoU
scores on SYNTHIA$\rightarrow$Cityscapes, respectively. Besides, compared with
the traditional domain adaptation approaches that utilize the source data, our
method achieves comparable or superior results. These results verify the
effectiveness of our method.

In Table III, we show results on Cityscapes$\rightarrow$Cross-City task that
has a minor domain shift attribute to both source and target data obtained
from real-world datasets. We also compare with conventional source data-
dependent domain adaptation approaches [95, 90, 10, 7] and source data-free
approaches [57, 81] that provide adaptation results in the original paper.
From the results, we can observe that our method ATP achieves the best
performance in four different adaptation scenarios, including from Cityscapes
to Rome, Rio, Tokyo, and Taipei, respectively. It demonstrates the
effectiveness of our method for minor domain shift adaptation.

TABLE III: Results of Cityscapes $\rightarrow$ Cross-City domain adaptation. “SF” denotes the source data-free setting. Red is the best result for source data-free methods. Blue is the best result for source data-dependent methods. City | Method | SF |  road |  side. |  build. |  light |  sign |  vege. |  sky |  person |  rider |  car |  bus |  motor. |  bike | mIoU
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Rome |  ADVENT [95] | ✗ | - | - | - | - | - | - | - | - | - | - | - | - | - | 47.3
AdaptSegNet [90] | - | - | - | - | - | - | - | - | - | - | - | - | - | 48.0
Cross-City [10] | 79.5 | 29.3 | 84.5 | 0.0 | 22.2 | 80.6 | 82.8 | 29.5 | 13.0 | 71.7 | 37.5 | 25.9 | 1.0 | 42.9
MaxSquare [7] | 82.9 | 32.6 | 86.7 | 20.7 | 41.6 | 85.0 | 93.0 | 47.2 | 22.5 | 82.2 | 53.8 | 50.5 | 9.9 | 54.5
SFDA [57] |  | - | - | - | - | - | - | - | - | - | - | - | - | - | 48.3
URMA [81] |  | 86.2 | 39.1 | 87.6 | 14.3 | 37.8 | 85.5 | 88.5 | 49.9 | 21.9 | 81.6 | 56.3 | 40.4 | 10.4 | 53.8
ATP (w/o TP) | ✓ | 80.7 | 28.6 | 85.2 | 13.7 | 32.6 | 83.3 | 85.8 | 38.9 | 9.5 | 78.2 | 36.9 | 43.5 | 9.7 | 48.2
ATP (w/o P) |  | 88.4 | 43.5 | 89.2 | 10.4 | 41.1 | 87.1 | 93.4 | 51.8 | 4.0 | 83.7 | 46.2 | 45.7 | 15.5 | 53.8
ATP |  | 90.2 | 46.5 | 89.7 | 15.5 | 40.2 | 87.4 | 93.9 | 54.7 | 15.5 | 85.9 | 48.1 | 48.6 | 10.2 | 55.9
Rio |  ADVENT [95] | ✗ | - | - | - | - | - | - | - | - | - | - | - | - | - | 46.8
AdaptSegNet [90] | - | - | - | - | - | - | - | - | - | - | - | - | - | 47.8
Cross-City [10] | 74.2 | 43.9 | 79.0 | 2.4 | 7.5 | 77.8 | 69.5 | 39.3 | 10.3 | 67.9 | 41.2 | 27.9 | 10.9 | 42.5
MaxSquare [7] | 76.9 | 48.8 | 85.2 | 13.8 | 18.9 | 81.7 | 88.1 | 54.9 | 34.0 | 76.8 | 39.8 | 44.1 | 29.7 | 53.3
SFDA [57] |  | - | - | - | - | - | - | - | - | - | - | - | - | - | 49.0
URMA [81] |  | 82.8 | 57.0 | 84.8 | 17.4 | 24.0 | 80.5 | 86.0 | 54.2 | 27.7 | 78.2 | 43.8 | 38.3 | 21.5 | 53.5
ATP (w/o TP) | ✓ | 73.4 | 44.1 | 82.9 | 9.5 | 14.0 | 79.1 | 81.2 | 50.5 | 10.8 | 71.4 | 32.3 | 47.2 | 36.9 | 48.7
ATP (w/o P) |  | 84.7 | 58.5 | 86.3 | 0.8 | 26.5 | 82.0 | 88.1 | 56.7 | 23.6 | 78.1 | 38.1 | 50.4 | 34.4 | 55.3
ATP |  | 85.8 | 60.0 | 87.8 | 0.0 | 29.9 | 83.6 | 88.7 | 60.7 | 23.3 | 76.4 | 43.0 | 47.4 | 41.4 | 57.8
Tokyo |  ADVENT [95] | ✗ | - | - | - | - | - | - | - | - | - | - | - | - | - | 45.5
AdaptSegNet [90] | - | - | - | - | - | - | - | - | - | - | - | - | - | 46.2
Cross-City [10] | 83.4 | 35.4 | 72.8 | 12.3 | 12.7 | 77.4 | 64.3 | 42.7 | 21.5 | 64.1 | 20.8 | 8.9 | 40.3 | 42.8
MaxSquare [7] | 81.2 | 30.1 | 77.0 | 12.3 | 27.3 | 82.8 | 89.5 | 58.2 | 32.7 | 71.5 | 5.5 | 37.4 | 48.9 | 50.5
SFDA [57] |  | - | - | - | - | - | - | - | - | - | - | - | - | - | 46.4
URMA [81] |  | 87.1 | 38.3 | 77.2 | 13.7 | 24.4 | 82.6 | 86.9 | 54.1 | 28.0 | 69.6 | 18.5 | 19.2 | 48.0 | 49.8
ATP (w/o TP) | ✓ | 79.0 | 25.1 | 75.0 | 10.5 | 21.1 | 80.3 | 85.5 | 48.9 | 17.2 | 67.9 | 5.6 | 24.8 | 47.0 | 45.3
ATP (w/o P) |  | 88.1 | 38.2 | 79.8 | 0.1 | 22.6 | 84.5 | 89.2 | 55.5 | 22.7 | 72.0 | 3.7 | 21.3 | 52.7 | 48.6
ATP |  | 89.9 | 40.5 | 83.3 | 0.0 | 27.5 | 85.4 | 91.7 | 59.6 | 22.4 | 75.4 | 4.3 | 14.3 | 55.1 | 50.5
Taipei |  ADVENT [95] | ✗ | - | - | - | - | - | - | - | - | - | - | - | - | - | 45.1
AdaptSegNet [90] | - | - | - | - | - | - | - | - | - | - | - | - | - | 45.1
Cross-City [10] | 78.6 | 28.6 | 80.0 | 13.1 | 7.6 | 68.2 | 82.1 | 16.8 | 9.4 | 60.4 | 34.0 | 26.5 | 9.9 | 39.6
MaxSquare [7] | 80.7 | 32.5 | 85.5 | 32.7 | 15.1 | 78.1 | 91.3 | 32.9 | 7.6 | 69.5 | 44.8 | 52.4 | 34.9 | 50.6
SFDA [57] |  | - | - | - | - | - | - | - | - | - | - | - | - | - | 47.2
URMA [81] |  | 86.4 | 34.6 | 84.6 | 22.4 | 9.9 | 76.2 | 88.3 | 32.8 | 15.1 | 74.8 | 45.8 | 53.3 | 26.7 | 50.1
ATP (w/o TP) | ✓ | 73.9 | 23.3 | 82.6 | 20.1 | 12.2 | 74.3 | 86.6 | 23.2 | 0.8 | 63.0 | 35.2 | 41.5 | 31.7 | 43.6
ATP (w/o P) |  | 88.1 | 37.4 | 86.7 | 0.0 | 10.2 | 79.6 | 93.2 | 38.3 | 3.3 | 75.9 | 43.1 | 55.8 | 33.3 | 49.6
ATP |  | 88.5 | 39.8 | 84.6 | 0.0 | 11.9 | 81.0 | 78.6 | 43.5 | 2.6 | 79.4 | 51.2 | 58.7 | 41.3 | 51.1

###  4.4 Extension for Black-box Source Model

In Table IV, we provide the results of the black-box source model case where
only the source model’s predictions are available. This situation is more
challenging and interesting in practice because it does not require any source
model details but the network outputs. Due to no access to trained source
model parameters, we use knowledge distill to enforce the target model to
learn similarity predictions with the source model, which is considered as the
baseline model with 38.6% mIoU performance. After that, we apply the proposed
bidirectional self-training strategy and information propagation to tackle the
black-box source model scenario. “ATP (w/o P)” and “ATP” represent our black-
box source model on the bidirectional self-training stage and information
propagation stage, respectively. Furthermore, we apply data augmentation
techniques like “Color Jitter” and “Gaussian Blur” to enhance feature
representations. “ATP (aug)” denotes the corresponding results. In Table IV,
we compare the black-box source model with conventional domain adaptation and
source data-free domain adaptation approaches. From the results, we can
observe that our method is suitable for the black-box source model situation
with a performance of 45.3%, achieving comparable results to source data-
dependent and source data-free adaptation methods.

###  4.5 Ablation Study

In this section, we provide extensive experiments to verify the effectiveness
of the proposed ATP. Specifically, we first reveal the improvement of proposed
components on GTA5$\rightarrow$Cityscapes task by progressively adding each
module into the system. As Table V shows, we can observe that each component
contributes to adaptation, with achieving the final performance of 52.6% mIoU
on the source data-free scenario. Then, we conduct experiments on different
segmentation backbones to verify the generalization of our method. The details
are described as follows.

TABLE IV: Results of _black-box_ source model on GTA5 $\rightarrow$ Cityscapes
domain adaptation. “SF” denotes the source data-free setting. Gray row group
denotes results of our black-box source model.

Method | SF |  road |  side. |  build. |  wall |  fence |  pole |  light |  sign |  vege. |  terr. |  sky |  person |  rider |  car |  truck |  bus |  train |  motor. |  bike |  mIoU
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
AdaptSegNet [40] |  | 86.5 | 36.0 | 79.9 | 23.4 | 23.3 | 23.9 | 35.2 | 14.8 | 83.4 | 33.3 | 75.6 | 58.5 | 27.6 | 73.7 | 32.5 | 35.4 | 3.9 | 30.1 | 28.1 | 42.4
ADVENT [43] |  | 89.4 | 33.1 | 81.0 | 26.6 | 26.8 | 27.2 | 33.5 | 24.7 | 83.9 | 36.7 | 78.8 | 58.7 | 30.5 | 84.8 | 38.5 | 44.5 | 1.7 | 31.6 | 32.4 | 45.5
CBST [57] | ✗ | 91.8 | 53.5 | 80.5 | 32.7 | 21.0 | 34.0 | 28.9 | 20.4 | 83.9 | 34.2 | 80.9 | 53.1 | 24.0 | 82.7 | 30.3 | 35.9 | 16.0 | 25.9 | 42.8 | 45.9
MaxSquare [4] |  | 89.4 | 43.0 | 82.1 | 30.5 | 21.3 | 30.3 | 34.7 | 24.0 | 85.3 | 39.4 | 78.2 | 63.0 | 22.9 | 84.6 | 36.4 | 43.0 | 5.5 | 34.7 | 33.5 | 46.4
PLCA [15] |  | 84.0 | 30.4 | 82.4 | 35.3 | 24.8 | 32.2 | 36.8 | 24.5 | 85.5 | 37.2 | 78.6 | 66.9 | 32.8 | 85.5 | 40.4 | 48.0 | 8.8 | 29.8 | 41.8 | 47.7
SFDA [25] |  | 84.2 | 39.2 | 82.7 | 27.5 | 22.1 | 25.9 | 31.1 | 21.9 | 82.4 | 30.5 | 85.3 | 58.7 | 22.1 | 80.0 | 33.1 | 31.5 | 3.6 | 27.8 | 30.6 | 43.2
URMA [35] | ✓ | 92.3 | 55.2 | 81.6 | 30.8 | 18.8 | 37.1 | 17.7 | 12.1 | 84.2 | 35.9 | 83.8 | 57.7 | 24.1 | 81.7 | 27.5 | 44.3 | 6.9 | 24.1 | 40.4 | 45.1
S4T [78] |  | 89.7 | 39.2 | 84.4 | 25.7 | 29.0 | 39.5 | 45.1 | 36.8 | 86.8 | 41.8 | 79.3 | 61.2 | 26.7 | 85.0 | 19.3 | 28.2 | 5.3 | 11.8 | 9.3 | 44.8
Baseline | - | 75.5 | 25.4 | 80.8 | 20.2 | 25.0 | 10.9 | 17.5 | 0.0 | 84.4 | 16.0 | 83.5 | 58.7 | 0.0 | 80.5 | 34.9 | 46.1 | 0.0 | 31.9 | 42.0 | 38.6
ATP (w/o P) |  | 74.8 | 23.6 | 78.9 | 27.4 | 17.5 | 24.1 | 34.0 | 24.9 | 82.6 | 25.7 | 74.3 | 58.6 | 31.7 | 71.5 | 31.4 | 6.9 | 0.0 | 34.8 | 45.4 | 40.4
ATP | ✓ | 83.3 | 24.2 | 80.3 | 29.7 | 22.4 | 26.3 | 33.0 | 25.5 | 84.7 | 35.7 | 76.6 | 61.9 | 34.0 | 81.7 | 32.1 | 17.3 | 0.0 | 42.1 | 53.4 | 44.4
ATP (aug) |  | 83.6 | 25.8 | 81.9 | 30.2 | 25.2 | 27.9 | 36.2 | 28.7 | 84.8 | 34.4 | 77.5 | 62.2 | 35.7 | 81.5 | 32.3 | 16.8 | 0.0 | 41.7 | 53.5 | 45.3

TABLE V: Ablation study of each component on GTA5$\rightarrow$ Cityscapes domain adaptation task. Model | $\mathcal{L}_{cel}$ | $\mathcal{L}_{div}$ | $\mathcal{L}_{div}^{*}$ | $\mathcal{L}_{ppl}$ | $\mathcal{L}_{npl}$ | $\mathcal{L}_{ssl}$ | mIoU
---|---|---|---|---|---|---|---
Baseline |  |  |  |  |  |  | 39.3
(a) | ✓ |  |  |  |  |  | 43.6
(b) | ✓ | ✓ |  |  |  |  | 45.0
(c) | ✓ |  | ✓ |  |  |  | 43.9
(d) | ✓ | ✓ |  | ✓ |  |  | 47.7
(e) | ✓ | ✓ |  | ✓ | ✓ |  | 49.8
(f) | ✓ | ✓ |  | ✓ | ✓ | ✓ | 52.6
TABLE VI: Results on different SSL approaches on GTA5 $\rightarrow$ Cityscapes task. $ratio$ | Baseline | CutMix [113] | ClassMix [70] | ReCo [56]
---|---|---|---|---
0.5 | 49.8 | 50.4 | 52.6 | 51.9
0.3 | 49.8 | 50.6 | 52.1 | 51.4

Influence of implicit feature alignment. From the models (a) and (b) in Table
V, we can observe that our curriculum-style entropy loss ($\mathcal{L}_{cel}$)
and weighted diversity loss ($\mathcal{L}_{div}$) can significantly improve
adaptation performance. Specifically, model (a) with $\mathcal{L}_{cel}$
achieves a 43.6% mIoU result, which surpasses the baseline model by 4.3
points. It also significantly outperforms the previous entropy minimization
method [81] reported mIoU result as 19.8%. Furthermore, model (b) with
$\mathcal{L}_{div}$ boosts the performance improvement with 1.4 mIoU,
revealing that diversity enforcing is important for our setting.
$\mathcal{L}_{div}^{*}$ denotes diversity loss provided by [53] with the mean
output embedding. Compared model (b) with (c), we see that both diversity-
promoting terms are beneficial for our task, and the proposed weighted one
($\mathcal{L}_{div}$) works better than a non-weighted one
($\mathcal{L}_{div}^{*}$).

Influence of bidirectional self-training. To verify the effectiveness of the
proposed bidirectional self-training method, we ablate the positive pseudo
labeling $\mathcal{L}_{ppl}$ and negative pseudo labeling $\mathcal{L}_{npl}$,
respectively. The results show that model (d) trained from the only positive
pseudo labeling provides 2.7 point gains compared to model (b). It is
reasonable that the positive pseudo labels provide useful supervised
information for the target data. On the other hand, adding the negative pseudo
labeling (model (e)) fancily offers 2.1 point improvement further. This result
demonstrates that negative pseudo labels can provide complementary information
which is ignored by the traditional positive pseudo labeling methods. Both the
positive and negative pseudo labeling are helpful to learn decision
boundaries.

Influence of information propagation. To testify the effectiveness of the
information propagation strategy, we follow several semi-supervised learning
methods in our approach, including consistency learning technique (CutMix
[113], ClassMix [70]) and contrastive learning technique (ReCo [56]). We
report the best result obtained from ClassMix model (f) in Table V, which
provides a significant improvement with 2.8 mIoU compared to model (e). It
demonstrates that the semi-supervised technique is simple and effective for
reducing intra-domain discrepancy. Detailed results of the information
propagation stage are shown in Table VI. In this subsection, two consistency
learning based methods (CutMix [113], ClassMix [70]) and one contrastive
learning based method (ReCo [56]) are adopted. The parameter $ratio$ denotes
the proportion of easy split on the whole target dataset.

Figure 4: Visualization for predicted segmentation masks on the
GTA5$\rightarrow$Cityscapes task. Figure 5: Analysis for the hyper-parameters
$\alpha$ and $\gamma$ of the proposed adaptive entropy loss.

Performance on different backbones. To demonstrate the robustness of our
method, we apply different backbones to conduct the semantic segmentation
network. In this section, we utilize VGG-16 [85] networks as a segmentation
feature extractor, and the final adaptation performance is shown in Table VII.
We compare existing source data-dependent domain adaptation works that report
results based on VGG-16 networks. We can observe that our method surpasses
existing conventional source data-dependent domain adaptation approaches with
achieving 39.8% mIoU, which reveals the proposed ATP is robust for different
backbone networks.

TABLE VII: Domain adaptation results of GTA5$\rightarrow$Cityscapes task based on VGG-16 network. Method |  SF |  mIoU
---|---|---
Baseline |  - |  27.1
ADVENT [95] |  ✗ |  35.6
AdaptSegNet [90] |  ✗ |  35.0
CLAN [63] |  ✗ |  36.6
CyCADA [32] |  ✗ |  35.4
PatchDA [91] |  ✗ |  37.5
SSF-DAN [17] |  ✗ |  37.7
ATP (Ours) |  ✓ |  39.8

###  4.6 Parameter Sensitivity Analysis

Our framework contains two new hyper-parameters $\alpha$ and $\gamma$ in Eq.
(2). To analyze the influence, we conduct experiments on the
GTA5$\rightarrow$Cityscapes task. We first freeze $\gamma$ and choose
$\alpha=0.002$ from the range of [0.001, 0.002, 0.003], and then the value of
$\gamma$ is selected as 3 from the range of [0, 1, 2, 3, 4, 5]. The results
are shown in Figure 5. Although all experiments set $\alpha=0.002$ and
$\gamma=3$ since it peaks, we observe that the proposed curriculum-style
entropy loss is relatively robust to these parameters. Besides, we also
optimize the model without freezing the source classifier. The performance
drops significantly compared to those with frozen classifier, which indicates
that the hypothesis transfer with frozen classifier is essential.

Figure 6 shows results when varying the assignment threshold $\lambda_{neg}$
related to negative pseudo labels in Eq. (6). It shows that the proposed
negative pseudo labeling method is relatively robust to this hyper-parameter.
Moreover, the proposed method quickly converges after several training epochs.
We also observe that $\lambda_{neg}<0.1$ obtains a satisfactory performance
compared to those when $\lambda_{neg}>0.1$. The reason is that more noise
labels are introduced when the threshold increases as our negative pseudo
labeling method focuses on predictions with lower confidence scores.

TABLE VIII: Results of different pseudo-labeling thresholds in Eq. (4) on GTA5$\rightarrow$Cityscapes task. Top-$K$ | 0.85 | 0.80 | 0.75 | 0.70 | 0.65 | 0.60
---|---|---|---|---|---|---
mIoU | 47.5 | 47.4 | 47.0 | 47.3 | 47.7 | 47.1
Figure 6: Robustness to the negative threshold $\lambda_{neg}$ for the negative pseudo assignment method. TABLE IX: Results of different weights $\lambda$ for weighted diversity loss in Eq. (3) on GTA5$\rightarrow$Ciyscapes task. $\lambda$ | 1 | 2 | 3 | 4 | 5
---|---|---|---|---|---
mIoU | 44.0 | 44.2 | 45.0 | 44.1 | 44.0

More hyper-parameters introduced in this paper are analyzed in this
subsection. We provide the risk of target supervision leak in the
bidirectional self-training stage. For example, in Table VIII, we report the
influence of the pseudo-labeling threshold $\lambda_{c}$ in Eq. (4), which is
determined by the top-$K$. In Table IX, we also analyze the weight $\lambda$
for weighted diversity loss in Eq. (3), which controls the influence of
weighted mean output $\hat{p}_{x_{t}}^{(h,w,c)}$.

###  4.7 Visualization

In Figure 4, we visualize predicted segmentation masks obtained from our
method and compare the results to the masks predicted by the “Source-Only”
network. To show the contribution of each stage, we add each component
progressively and provide qualitative results of “ATP (w/o TP)”, “ATP (w/o
P)”, and “ATP”, respectively. It is evident that even without source data, our
method achieves reliable results with fewer spurious areas. Furthermore,
compared to the “Source Only” model, each stage contributes significantly in
terms of pixel-level accuracy.

##  5 Conclusion

This paper presents a novel source data-free framework for domain adaptive
semantic segmentation called ATP. In the absence of source data, ATP focuses
on transferring knowledge from the source model via implicit feature
alignment, teaching from positive and negative pseudo labels, and propagating
target-specific information to reduce the intra-domain discrepancy. Extensive
experiments and ablation studies are conducted to validate the effectiveness
of the proposed ATP. On the standard adaptation tasks, ATP achieves new state-
of-the-art results and performs comparably to source data-dependent adaptation
methods. It also performs well on the black-box source model scenario.

In the future, we will attempt to tackle two critical problems. The first is
to improve the performance of hard categories. As Table I and Table II show,
the proposed method fails to handle some classes with a large domain gap or
occupy a minor pixel proportion, like fence, pole, signal, and train. It is a
common but serious problem to address for DASS. The second is the selection of
hyper-parameters. In our work, we introduce amounts of hyper-parameters that
influence the final performance. It is essential to provide a method to learn
overall generality and robust hyper-parameter in future work.

## Acknowledgement

This work is jointly supported by the Major Project for New Generation of AI
(No.2018AAA0100400), and the National Natural Science Foundation of China (No.
61836014, No. U21B2042, No. 62072457, No. 62006231).

## References

  * [1] S. M. Ahmed, D. S. Raychaudhuri, S. Paul, S. Oymak, and A. K. Roy-Chowdhury.  Unsupervised multi-source domain adaptation without access to source data.  In Proc. CVPR, pages 10103–10112, 2021.
  * [2] D. Berthelot, N. Carlini, E. D. Cubuk, A. Kurakin, K. Sohn, H. Zhang, and C. Raffel.  Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring.  In Proc. ICLR, 2019.
  * [3] K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan.  Domain separation networks.  In Proc. NeurIPS, volume 29, 2016.
  * [4] L. Bruzzone and M. Marconcini.  Domain adaptation problems: A dasvm classification technique and a circular validation strategy.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(5):770–787, 2009.
  * [5] W.-L. Chang, H.-P. Wang, W.-H. Peng, and W.-C. Chiu.  All about structure: Adapting structural information across domains for boosting semantic segmentation.  In Proc. CVPR, pages 1900–1909, 2019.
  * [6] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille.  Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4):834–848, 2017.
  * [7] M. Chen, H. Xue, and D. Cai.  Domain adaptation for semantic segmentation with maximum squares loss.  In Proc. ICCV, pages 2090–2099, 2019.
  * [8] Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool.  Domain adaptive faster r-cnn for object detection in the wild.  In Proc. CVPR, pages 3339–3348, 2018.
  * [9] Y.-C. Chen, Y.-Y. Lin, M.-H. Yang, and J.-B. Huang.  Crdoco: Pixel-level domain transfer with cross-domain consistency.  In Proc. CVPR, pages 1791–1800, 2019.
  * [10] Y.-H. Chen, W.-Y. Chen, Y.-T. Chen, B.-C. Tsai, Y.-C. Frank Wang, and M. Sun.  No more discrimination: Cross city adaptation of road scene segmenters.  In Proc. ICCV, pages 1992–2001, 2017.
  * [11] Y. Cheng, F. Wei, J. Bao, D. Chen, F. Wen, and W. Zhang.  Dual path learning for domain adaptation of semantic segmentation.  In Proc. ICCV, pages 9082–9091, 2021.
  * [12] B. Chidlovskii, S. Clinchant, and G. Csurka.  Domain adaptation in the absence of source domain data.  In Proc. KDD, pages 451–460, 2016.
  * [13] J. Choi, T. Kim, and C. Kim.  Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation.  In Proc. ICCV, pages 6830–6840, 2019.
  * [14] S. Cicek and S. Soatto.  Unsupervised domain adaptation via regularized conditional alignment.  In Proc. ICCV, pages 1416–1425, 2019.
  * [15] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele.  The cityscapes dataset for semantic urban scene understanding.  In Proc. CVPR, pages 3213–3223, 2016.
  * [16] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei.  Imagenet: A large-scale hierarchical image database.  In Proc. CVPR, pages 248–255, 2009.
  * [17] L. Du, J. Tan, H. Yang, J. Feng, X. Xue, Q. Zheng, X. Ye, and X. Zhang.  Ssf-dan: Separated semantic feature based domain adaptation network for semantic segmentation.  In Proc. ICCV, pages 982–991, 2019.
  * [18] Y. Fan, R. He, J. Liang, and B. Hu.  Self-paced learning: An implicit regularization perspective.  In Proc. AAAI, volume 31, 2017.
  * [19] H. Feng, M. Chen, J. Hu, D. Shen, H. Liu, and D. Cai.  Complementary pseudo labels for unsupervised domain adaptation on person re-identification.  IEEE Transactions on Image Processing, 30:2898–2907, 2021.
  * [20] R. Flamary, N. Courty, D. Tuia, and A. Rakotomamonjy.  Optimal transport for domain adaptation.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(9):1853–1865, 2017.
  * [21] Y. Fu, Y. Wei, G. Wang, Y. Zhou, H. Shi, and T. S. Huang.  Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification.  In Proc. ICCV, pages 6112–6121, 2019.
  * [22] Y. Ganin and V. Lempitsky.  Unsupervised domain adaptation by backpropagation.  In Proc. ICML, pages 1180–1189, 2015.
  * [23] Y. Ge, F. Zhu, D. Chen, R. Zhao, et al.  Self-paced contrastive learning with hybrid memory for domain adaptive object re-id.  In Proc. NeurIPS, volume 33, pages 11309–11321, 2020.
  * [24] M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and W. Li.  Deep reconstruction-classification networks for unsupervised domain adaptation.  In Proc. ECCV, pages 597–613, 2016.
  * [25] B. Gong, K. Grauman, and F. Sha.  Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation.  In Proc. ICML, pages 222–230, 2013.
  * [26] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio.  Generative adversarial nets.  In Proc. NeurIPS, volume 27, 2014.
  * [27] R. Gopalan, R. Li, V. M. Patel, and R. Chellappa.  Domain adaptation for visual recognition.  Foundations and Trends® in Computer Graphics and Vision, 8(4):285–378, 2015.
  * [28] D. Guan, J. Huang, A. Xiao, S. Lu, and Y. Cao.  Uncertainty-aware unsupervised domain adaptation in object detection.  IEEE Transactions on Multimedia, 2021.
  * [29] J. Guo, X. Zhu, Z. Lei, and S. Z. Li.  Decomposed meta batch normalization for fast domain adaptation in face recognition.  IEEE Transactions on Information Forensics and Security, 16:3082–3095, 2021.
  * [30] K. He, X. Zhang, S. Ren, and J. Sun.  Deep residual learning for image recognition.  In Proc. CVPR, pages 770–778, 2016.
  * [31] G. Hinton, O. Vinyals, J. Dean, et al.  Distilling the knowledge in a neural network.  arXiv preprint arXiv:1503.02531, 2(7), 2015.
  * [32] J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. Efros, and T. Darrell.  CyCADA: Cycle-consistent adversarial domain adaptation.  In Proc. ICML, pages 1989–1998, 2018.
  * [33] J. Hoffman, D. Wang, F. Yu, and T. Darrell.  Fcns in the wild: Pixel-level adversarial and constraint-based adaptation.  arXiv preprint arXiv:1612.02649, 2016.
  * [34] W. Hong, Z. Wang, M. Yang, and J. Yuan.  Conditional generative adversarial network for structured domain adaptation.  In Proc. CVPR, pages 1335–1344, 2018.
  * [35] H. Huang, Q. Huang, and P. Krahenbuhl.  Domain transfer through deep activation matching.  In Proc. ECCV, pages 590–605, 2018.
  * [36] J. Huang, D. Guan, A. Xiao, and S. Lu.  Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data.  In Proc. NeurIPS, volume 34, 2021.
  * [37] J. Huang, S. Lu, D. Guan, and X. Zhang.  Contextual-relation consistent domain adaptation for semantic segmentation.  In Proc. ECCV, pages 705–722, 2020.
  * [38] W. C. Hung, Y. H. Tsai, Y. T. Liou, Y. Y. Lin, and M. H. Yang.  Adversarial learning for semi-supervised semantic segmentation.  In Proc. BMVC, 2019.
  * [39] M. Kan, J. Wu, S. Shan, and X. Chen.  Domain adaptation for face recognition: Targetize source domain bridged by common subspace.  International Journal of Computer Vision, 109(1):94–109, 2014.
  * [40] G. Kang, L. Jiang, Y. Yang, and A. G. Hauptmann.  Contrastive adaptation network for unsupervised domain adaptation.  In Proc. CVPR, pages 4893–4902, 2019.
  * [41] G. Kang, Y. Wei, Y. Yang, Y. Zhuang, and A. Hauptmann.  Pixel-level cycle association: A new perspective for domain adaptive semantic segmentation.  In Proc. NeurIPS, pages 3569–3580, 2020.
  * [42] M. Kim and H. Byun.  Learning texture invariant representation for domain adaptation of semantic segmentation.  In Proc. CVPR, pages 12975–12984, 2020.
  * [43] M. Kumar, B. Packer, and D. Koller.  Self-paced learning for latent variable models.  In Proc. NeurIPS, pages 1189–1197, 2010.
  * [44] J. N. Kundu, A. Kulkarni, A. Singh, V. Jampani, and R. V. Babu.  Generalize then adapt: Source-free domain adaptive semantic segmentation.  In Proc. ICCV, pages 7046–7056, 2021.
  * [45] J. N. Kundu, N. Venkat, R. V. Babu, et al.  Universal source-free domain adaptation.  In Proc. CVPR, pages 4544–4553, 2020.
  * [46] V. K. Kurmi and V. P. Namboodiri.  Looking back at labels: A class based domain adaptation technique.  In Proc. IJCNN, pages 1–8. IEEE, 2019.
  * [47] S. Lee, D. Kim, N. Kim, and S.-G. Jeong.  Drop to adapt: Learning discriminative features for unsupervised domain adaptation.  In Proc. ICCV, pages 91–100, 2019.
  * [48] R. Li, Q. Jiao, W. Cao, H.-S. Wong, and S. Wu.  Model adaptation: Unsupervised domain adaptation without source data.  In Proc. CVPR, pages 9641–9650, 2020.
  * [49] Y. Li, L. Yuan, and N. Vasconcelos.  Bidirectional learning for domain adaptation of semantic segmentation.  In Proc. CVPR, pages 6936–6945, 2019.
  * [50] Q. Lian, F. Lv, L. Duan, and B. Gong.  Constructing self-motivated pyramid curriculums for cross-domain semantic segmentation: A non-adversarial approach.  In Proc. ICCV, pages 6758–6767, 2019.
  * [51] J. Liang, R. He, Z. Sun, and T. Tan.  Aggregating randomized clustering-promoting invariant projections for domain adaptation.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(5):1027–1042, 2018.
  * [52] J. Liang, R. He, Z. Sun, and T. Tan.  Distant supervised centroid shift: A simple and efficient approach to visual domain adaptation.  In Proc. CVPR, pages 2975–2984, 2019.
  * [53] J. Liang, D. Hu, and J. Feng.  Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation.  In Proc. ICML, pages 6028–6039, 2020.
  * [54] J. Liang, D. Hu, J. Feng, and R. He.  Dine: Domain adaptation from single and multiple black-box predictors.  In Proc. CVPR, 2022.
  * [55] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár.  Focal loss for dense object detection.  In Proc. ICCV, pages 2980–2988, 2017.
  * [56] S. Liu, S. Zhi, E. Johns, and A. J. Davison.  Bootstrapping semantic segmentation with regional contrast.  arXiv preprint arXiv:2104.04465, 2021.
  * [57] Y. Liu, W. Zhang, and J. Wang.  Source-free domain adaptation for semantic segmentation.  In Proc. CVPR, pages 1215–1224, 2021.
  * [58] J. Long, E. Shelhamer, and T. Darrell.  Fully convolutional networks for semantic segmentation.  In Proc. CVPR, pages 3431–3440, 2015.
  * [59] M. Long, Y. Cao, Z. Cao, J. Wang, and M. I. Jordan.  Transferable representation learning with deep adaptation networks.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12):3071–3085, 2018.
  * [60] M. Long, Y. Cao, J. Wang, and M. Jordan.  Learning transferable features with deep adaptation networks.  In Proc. ICML, pages 97–105, 2015.
  * [61] M. Long, Z. Cao, J. Wang, and M. I. Jordan.  Conditional adversarial domain adaptation.  In Proc. NeurIPS, volume 31, 2018.
  * [62] W. Luo and M. Yang.  Semi-supervised semantic segmentation via strong-weak dual-branch network.  In Proc. ECCV, pages 784–800, 2020.
  * [63] Y. Luo, L. Zheng, T. Guan, J. Yu, and Y. Yang.  Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation.  In Proc. CVPR, pages 2507–2516, 2019.
  * [64] F. Maria Carlucci, L. Porzi, B. Caputo, E. Ricci, and S. Rota Bulo.  Autodial: Automatic domain alignment layers.  In Proc. ICCV, pages 5067–5075, 2017.
  * [65] K. Mei, C. Zhu, J. Zou, and S. Zhang.  Instance adaptive self-training for unsupervised domain adaptation.  In Proc. ECCV, pages 415–430, 2020.
  * [66] D. Mekhazni, A. Bhuiyan, G. Ekladious, and E. Granger.  Unsupervised domain adaptation in the dissimilarity space for person re-identification.  In Proc. ECCV, pages 159–174, 2020.
  * [67] S. Mittal, M. Tatarchenko, and T. Brox.  Semi-supervised semantic segmentation with high-and low-level consistency.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(4):1369–1379, 2019.
  * [68] T. Miyato, S.-i. Maeda, M. Koyama, and S. Ishii.  Virtual adversarial training: a regularization method for supervised and semi-supervised learning.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(8):1979–1993, 2018.
  * [69] Z. Murez, S. Kolouri, D. Kriegman, R. Ramamoorthi, and K. Kim.  Image to image translation for domain adaptation.  In Proc. CVPR, pages 4500–4509, 2018.
  * [70] V. Olsson, W. Tranheden, J. Pinto, and L. Svensson.  Classmix: Segmentation-based data augmentation for semi-supervised learning.  In Proc. WACV, pages 1369–1378, 2021.
  * [71] Y. Ouali, C. Hudelot, and M. Tami.  Semi-supervised semantic segmentation with cross-consistency training.  In Proc. CVPR, pages 12674–12684, 2020.
  * [72] F. Pan, I. Shin, F. Rameau, S. Lee, and I. S. Kweon.  Unsupervised intra-domain adaptation for semantic segmentation through self-supervision.  In Proc. ICCV, pages 3764–3773, 2020.
  * [73] S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang.  Domain adaptation via transfer component analysis.  IEEE Transactions on Neural Networks, 22(2):199–210, 2010.
  * [74] S. J. Pan and Q. Yang.  A survey on transfer learning.  IEEE Transactions on Knowledge and Data Engineering, 22(10):1345–1359, 2009.
  * [75] S. Paul, Y.-H. Tsai, S. Schulter, A. K. Roy-Chowdhury, and M. Chandraker.  Domain adaptive semantic segmentation using weak labels.  In Proc. ECCV, pages 571–587. Springer, 2020.
  * [76] Z. Pei, Z. Cao, M. Long, and J. Wang.  Multi-adversarial domain adaptation.  In Proc. AAAI, pages 3934–3941, 2018.
  * [77] J. Peng, P. Wang, C. Desrosiers, and M. Pedersoli.  Self-paced contrastive learning for semi-supervised medical image segmentation with meta-labels.  In Proc. NeurIPS, volume 34, 2021.
  * [78] V. Prabhu, S. Khare, D. Kartik, and J. Hoffman.  S4t: Source-free domain adaptation for semantic segmentation via self-supervised selective self-training.  arXiv preprint arXiv:2107.10140, 2021.
  * [79] S. R. Richter, V. Vineet, S. Roth, and V. Koltun.  Playing for data: Ground truth from computer games.  In Proc. ECCV, pages 102–118, 2016.
  * [80] G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez.  The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes.  In Proc. CVPR, pages 3234–3243, 2016.
  * [81] P. T. S and F. Fleuret.  Uncertainty reduction for model adaptation in semantic segmentation.  In Proc. CVPR, pages 9613–9623, 2021.
  * [82] K. Saito, D. Kim, P. Teterwak, S. Sclaroff, T. Darrell, and K. Saenko.  Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density.  In Proc. ICCV, pages 9184–9193, 2021.
  * [83] K. Saito, Y. Ushiku, T. Harada, and K. Saenko.  Adversarial dropout regularization.  In Proc. ICLR, 2018.
  * [84] C. Sakaridis, D. Dai, and L. Van Gool.  Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation.  IEEE Transactions on Pattern Analysis and Machine Intelligence, (01):1–1, 2020.
  * [85] K. Simonyan and A. Zisserman.  Very deep convolutional networks for large-scale image recognition.  In Proc. ICLR, 2015.
  * [86] N. Souly, C. Spampinato, and M. Shah.  Semi supervised semantic segmentation using generative adversarial network.  In Proc. ICCV, pages 5688–5696, 2017.
  * [87] M. N. Subhani and M. Ali.  Learning from scale-invariant examples for domain adaptation in semantic segmentation.  In Proc. ECCV, pages 290–306, 2020.
  * [88] M. Sugiyama, S. Nakajima, H. Kashima, P. Buenau, and M. Kawanabe.  Direct importance estimation with model selection and its application to covariate shift adaptation.  In Proc. NeurIPS, volume 20, 2007.
  * [89] T.-D. Truong, C. N. Duong, N. Le, S. L. Phung, C. Rainwater, and K. Luu.  Bimal: Bijective maximum likelihood approach to domain adaptation in semantic scene segmentation.  In Proc. ICCV, pages 8548–8557, 2021.
  * [90] Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker.  Learning to adapt structured output space for semantic segmentation.  In Proc. CVPR, pages 7472–7481, 2018.
  * [91] Y.-H. Tsai, K. Sohn, S. Schulter, and M. Chandraker.  Domain adaptation for structured output via discriminative patch representations.  In Proc. ICCV, pages 1456–1465, 2019.
  * [92] E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell.  Adversarial discriminative domain adaptation.  In Proc. CVPR, pages 7167–7176, 2017.
  * [93] V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz.  Interpolation consistency training for semi-supervised learning.  In Proc. IJCAI, pages 3635–3641, 2019.
  * [94] V. VS, V. Gupta, P. Oza, V. A. Sindagi, and V. M. Patel.  Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection.  In Proc. CVPR, pages 4516–4526, 2021.
  * [95] T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. Pérez.  Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation.  In Proc. CVPR, pages 2517–2526, 2019.
  * [96] M. Wang and W. Deng.  Deep visual domain adaptation: A survey.  Neurocomputing, 312:135–153, 2018.
  * [97] X. Wang, Y. Chen, and W. Zhu.  A survey on curriculum learning.  IEEE Transactions on Pattern Analysis and Machine Intelligence, (01):1–1, 2021.
  * [98] X. Wang, L. Li, W. Ye, M. Long, and J. Wang.  Transferable attention for domain adaptation.  In Proc. AAAI, volume 33, pages 5345–5352, 2019.
  * [99] Y. Wang, J. Peng, and Z. Zhang.  Uncertainty-aware pseudo label refinery for domain adaptive semantic segmentation.  In Proc. ICCV, pages 9092–9101, 2021.
  * [100] Z. Wang, M. Yu, Y. Wei, R. Feris, J. Xiong, W.-m. Hwu, T. S. Huang, and H. Shi.  Differential treatment for stuff and things: A simple unsupervised domain adaptation method for semantic segmentation.  In Proc. CVPR, pages 12635–12644, 2020.
  * [101] Z. Wu, X. Han, Y.-L. Lin, M. Gokhan Uzunbas, T. Goldstein, S. Nam Lim, and L. S. Davis.  Dcan: Dual channel-wise alignment networks for unsupervised scene adaptation.  In Proc. ECCV, pages 518–534, 2018.
  * [102] H. Xia, H. Zhao, and Z. Ding.  Adaptive adversarial network for source-free domain adaptation.  In Proc. ICCV, pages 9010–9019, 2021.
  * [103] Q. Xie, Z. Dai, E. Hovy, T. Luong, and Q. Le.  Unsupervised data augmentation for consistency training.  In Proc. NeurIPS, pages 6256–6268, 2020.
  * [104] H. Yan, Y. Guo, and C. Yang.  Augmented self-labeling for source-free unsupervised domain adaptation.  In NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications, 2021.
  * [105] F. Yang, K. Yan, S. Lu, H. Jia, D. Xie, Z. Yu, X. Guo, F. Huang, and W. Gao.  Part-aware progressive unsupervised domain adaptation for person re-identification.  IEEE Transactions on Multimedia, 23:1681–1695, 2020.
  * [106] J. Yang, W. An, S. Wang, X. Zhu, C. Yan, and J. Huang.  Label-driven reconstruction for domain adaptation in semantic segmentation.  In Proc. ECCV, pages 480–498, 2020.
  * [107] J. Yang, C. Li, W. An, H. Ma, Y. Guo, Y. Rong, P. Zhao, and J. Huang.  Exploring robustness of unsupervised domain adaptation in semantic segmentation.  In Proc. ICCV, pages 9194–9203, 2021.
  * [108] J. Yang, S. Shi, Z. Wang, H. Li, and X. Qi.  St3d: Self-training for unsupervised domain adaptation on 3d object detection.  In Proc. CVPR, pages 10368–10378, 2021.
  * [109] S. Yang, Y. Wang, J. van de Weijer, L. Herranz, and S. Jui.  Generalized source-free domain adaptation.  In Proc. ICCV, pages 8978–8987, 2021.
  * [110] Y. Yang and S. Soatto.  Fda: Fourier domain adaptation for semantic segmentation.  In Proc. CVPR, pages 4085–4095, 2020.
  * [111] M. Ye, J. Zhang, J. Ouyang, and D. Yuan.  Source data-free unsupervised domain adaptation for semantic segmentation.  In Proc. ACM MM, pages 2233–2242, 2021.
  * [112] J. Yuan, Y. Liu, C. Shen, Z. Wang, and H. Li.  A simple baseline for semi-supervised semantic segmentation with strong data augmentation.  In Proc. ICCV, pages 8229–8238, 2021.
  * [113] S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo.  Cutmix: Regularization strategy to train strong classifiers with localizable features.  In Proc. ICCV, pages 6023–6032, 2019.
  * [114] B. Zadrozny.  Learning and evaluating classifiers under sample selection bias.  In Proc. ICML, page 114, 2004.
  * [115] W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, and S. Saminger-Platz.  Central moment discrepancy (cmd) for domain-invariant representation learning.  In Proc. ICLR, 2017.
  * [116] P. Zhang, B. Zhang, T. Zhang, D. Chen, Y. Wang, and F. Wen.  Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation.  In Proc. CVPR, pages 12414–12424, 2021.
  * [117] Q. Zhang, J. Zhang, W. Liu, and D. Tao.  Category anchor-guided unsupervised domain adaptation for semantic segmentation.  In Proc. NeurIPS, pages 433–443, 2019.
  * [118] Y. Zhang, P. David, and B. Gong.  Curriculum domain adaptation for semantic segmentation of urban scenes.  In Proc. ICCV, pages 2020–2030, 2017.
  * [119] Y. Zhang, T. Liu, M. Long, and M. Jordan.  Bridging theory and algorithm for domain adaptation.  In Proc. ICML, pages 7404–7413, 2019.
  * [120] Y. Zhang, Z. Qiu, T. Yao, C.-W. Ngo, D. Liu, and T. Mei.  Transferring and regularizing prediction for semantic segmentation.  In Proc. CVPR, pages 9621–9630, 2020.
  * [121] Z. Zheng and Y. Yang.  Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation.  International Journal of Computer Vision, 129(4):1106–1120, 2021\.
  * [122] X. Zhu, H. Zhou, C. Yang, J. Shi, and D. Lin.  Penalizing top performers: Conservative loss for semantic segmentation adaptation.  In Proc. ECCV, pages 568–583, 2018.
  * [123] Y. Zou, Z. Yu, X. Liu, B. Kumar, and J. Wang.  Confidence regularized self-training.  In Proc. ICCV, pages 5982–5991, 2019.
  * [124] Y. Zou, Z. Yu, B. Vijaya Kumar, and J. Wang.  Unsupervised domain adaptation for semantic segmentation via class-balanced self-training.  In Proc. ECCV, pages 289–305, 2018.

|  Yuxi Wang received the Bachelor degree from North Eastern University,
China, in 2016, and the PhD degree from University of Chinese Academy of
Sciences (UCAS), Institute of Automation, Chinese Academy of Sciences (CASIA),
in January 2022. He is now an assistant professor in the Center for Artificial
Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation,
Chinese Academy of Science (HKISI-CAS). His research interests include
transfer learning, domain adaptation and computer vision.
---|---
|  Jian Liang received the B.E. degree in Electronic Information and
Technology from Xi’an Jiaotong University and Ph.D. degree in Pattern
Recognition and Intelligent Systems from NLPR, CASIA in July 2013, and January
2019, respectively. He was a research fellow at National University of
Singapore from June 2019 to April 2021. Now he joins NLPR as an associate
professor. His research interests focus on transfer learning, pattern
recognition, and computer vision.
---|---
|  Zhaoxiang Zhang Zhaoxiang Zhang received his bachelor’s degree from the
Department of Electronic Science and Technology in the University of Science
and Technology of China (USTC) in 2004. After that, he was a Ph.D. candidate
under the supervision of Professor Tieniu Tan in the National Laboratory of
Pattern Recognition, Institute of Automation, Chinese Academy of Sciences,
where he received his Ph.D. degree in 2009. In October 2009, he joined the
School of Computer Science and Engineering, Beihang University, as an
Assistant Professor (2009-2011), an Associate Professor (2012-2015) and the
Vise-Director of the Department of Computer application technology
(2014-2015). In July 2015, he returned to the Institute of Automation, Chinese
Academy of Sciences. He is now a full Professor in the Center for Research on
Intelligent Perception and Computing (CRIPAC) and the National Laboratory of
Pattern Recognition (NLPR). His research interests include Computer Vision,
Pattern Recognition, and Machine Learning. Recently, he specifically focuses
on deep learning models, biologically-inspired visual computing and human-like
learning, and their applications on human analysis and scene understanding. He
has published more than 200 papers in international journals and conferences,
including reputable international journals such as JMLR, IEEE TIP, IEEE TNN,
IEEE TCSVT, IEEE TIFS and top level international conferences like CVPR, ICCV,
NIPS, ECCV, AAAI, IJCAI and ACM MM. He is serving or has served as the
Associated Editor of IEEE T-CSVT, Patten Recognition, Neurocomputing, and
Frontiers of Computer Science. He has served as the Area Chair, Senior PC of
international conferences like CVPR, NIPS, ICML, AAAI, IJCAI and ACM MM. He is
a Senior Member of IEEE, a Distinguished Member of CCF, and a Distinguished
member of CAAI.
---|---