Balanced Learning for Domain Adaptive  Semantic Segmentation
Authors: Wangkai Li
Rui Sun
Bohao Liao
Zhaoyang Li
Tianzhu Zhang
Venue: ICML 2025 poster (2025)
Keywords: Semantic segmentation
Unsupervised domain adaptation
Primary area: applications->computer_vision

Abstract: Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
Despite the effectiveness of self-training techniques in UDA, they struggle to learn each class in a balanced manner due to inherent class imbalance and distribution shift in both data and label space between domains.
To address this issue, we propose Balanced Learning for Domain Adaptation (BLDA), a novel approach to directly assess and alleviate class bias without requiring prior knowledge about the distribution shift.
First, we identify over-predicted and under-predicted classes by analyzing the distribution of predicted logits.
Subsequently, we introduce a post-hoc approach to align the  logits distributions across different classes using shared anchor distributions.
To further consider the network's need to generate unbiased pseudo-labels during self-training, we estimate logits distributions online and incorporate logits correction terms into the loss function.
Moreover, we leverage the resulting cumulative density as domain-shared structural knowledge to connect the source and target domains.
Extensive experiments on two standard UDA semantic segmentation benchmarks demonstrate that BLDA consistently improves performance, especially for under-predicted classes, when integrated into various existing methods.

Balanced Learning for Domain Adaptive Semantic Segmentation
Wangkai Li Rui Sun
Bohao Liao
Zhaoyang Li
Tianzhu Zhang 12
Abstract
bead
cay to Sannaer
Unsupervised domain adaptation (UDA) for se- mantic segmentation aims to transfer knowledge from labeled source domain to an unlabeled target domain. Despite the effectiveness of self- training techniques in UDA, they struggle to learn each class in a balanced manner due to inherent class imbalance and distribution shift in both data and label space between domains. To address this issue, we propose Balanced Learning for Domain Adaptation (BLDA), novel approach to directly assess and alleviate class bias without requiring prior knowledge about the distribution shift. First, we identify over-predicted and inder-predicted classes by analyzing the distribution of predicted logits. Subsequently, we introduce post-hoc approach to align the logits distributions across different classes using shared anchor distributions. To further consider the network's 's need to generate unbiased pseudo-labels during self-training we estimate logits distributions online and incorpo- rate logits correction terms into the loss function. Moreover, we leverage the resulting cumulative density as domain-shared structural knowledge to connect the source and target domains. Exten- sive experiments on two standard UDA seman- tic segmentation benchmarks demonstrate that BLDA consistently improves performance, espe- cially for under-predicted classes, when integrated into various existing methods. Code is available at htttts://gttttti cwm/Wooff /BLDA.
Diacrepaccy
Diicspancy
Lagg Saglt
prodittia
I LOGHOBSR Deareess
(1) Loggs Distibation Obcrepancy
Diccrpancy
Figure 1: Demonstration of factors that cause class bias. (a) The inherent class imbalance problem in segmentation datasets. (b) The differences in transfer difficulty across classes in cross-domain settings. "Oracle' represents the performance under full supervision, while "Srr-only' repre- sents training with the source domain and testing it on the target domain. (c) The differences in logits distributions pre- dicted for each class, including "positive distribution" and "negative distribution".
(d) Bias assessment for different classes via Eq.4. The corresponding class IDs of (a), (b), and (c) are mapped in descending order onto this figure.
et al., 2021; 2022). However, the performance of these methods often degrades significantly when applied to new target domains due to differences between the source and target domains. Unsupervised domain adaptation (UDA) techniques have been extensively studied to address this issue by transferring knowledge from labeled source do- main unlabeled target domain, aiming to bridge the domain gap and improve the model's performance on the target dataset without requiring additional annotations.
1. Introduction
In previous work, self self-training techniques (Tranheden et al., 2021; Hoyer et al., 2022a) have been naturally introduced into UDA tasks to fully utilize the large amount of unlabeled target domain data, becoming a mainstream paradigm. This paradigm constructs teacher network using a temporal aggregation mechanism, treats its predictions on the target domain as pseudo-labels, and gradually guides the student network's 's learning. Despite achieving remarkable results, these methods struggle to learn each class in a balanced manner. Generally, the inherent class imbalance in segmen- tation datasets (Cordts al., 2016) (Fig. (a)) leads networks
Semantic segmentation, which assigns semantic labels to each pixel in an image, has made remarkable progress in recent years (Long et al., 5a; Chen et al., 20176 7b; Cheng
MMoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China 2 DDeep Space Exploration Laboratory. Correspondence Tianzhu Zhang <tzzhang @ wwt.uussuucc.... edu.en>.
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Balanced Learning for Domain Adaptive Semantic Segmentation
to produce biased predictions towards head classes, often studied as the long-tail problem (Van Horn & Perona, 2017; Buda et al., 2018; Liu et al., 2019). However, in UDA, data and label distribution shifts between the training and test data complicate the class bias. The network's bias towards classes does not entirely depend on the differences in class sample distribution. As shown in Fig. (bb, when a network trained on the source domain is tested on the target domain, the performance degradation varies greatly across classes, distinguishing easy-to-transfer and hard-to-transfer classes. These factors jointly determine the network's s different bi- ases towards each class in target domain, resulting in over- prediction and under- preeeerrriii.oo. Furthermore, confirmation bias (Guo et al., 2017) causes self-training techniques to exacerbate this phenomenon. 2ig. shows the severe dete- rioration of classes like rider and bicycle after self-training, widening the performance gap across classes. Therefore, achieving balanced learning for each class in UDA is a challenging and worthwhile exploration.
under spreditteeee
kaysl
[a] Clase-Wtte Acouacy
(b] Peouds tabel Frequancy
Figure 2: In UDA, class bias can be expressed as over- predicted classes and under-predicted classes. (a) Class- wise accuracy under different training settings. (b) Fre- queney of ppeudo-labele generated by the network for dif- ferent classes during training.
di differences in the predicted logits distributions for different classes, directly leading to class bias. Fdg. illustrates that the ranking of class bias highly coincides with the ranking of logit distribution differences, i.e., over-predicted classes have larger logit values, while under-predicted classes have smaller logit values. This assessment approach prompts us to propose BLDA, a method to achieve balanced learning for domain adaptive semantic segmentation by balancing the logits distribution. First, we consider a post-hoc approach to adjust the logits (Sec.3.3.3). We set shared anchor distri- butions for the positive and negative logits distributions and align the class-wise logits distributions with the anchor dis- tributions based on the cumulative density function mapping, Furthermore, to generate unbiased pseudo-labels for classes during self-training, we propose an online logit adjustment method (Sec.3.3.4). This strategy couples Gaussian mixture models to estimate the logits distributions online during training and incorporates logit correction terms into the loss function to replace the post-hoe method. Moreover, we find that the resulting cumulative density can measure the discrimination difficulty of different sample points in each class, which is a domain-shared structural knowledge that can be used as an auxiliary loss to connect the two domains, further enhancing domain adaptation performance (Sec.3.3.5). As shown in Fig.2, our method can be inte- grated into existing self-training-bart UDA paradigms and effectively balance the prediction bias across classes.
Existing strategies to reduce model bias towards different classes can be broadly categorized into re- reiittggn (Cui et al., 2019; Lin et al., 2017; Cao et al., 2019; Truong al., 2023; Buda al., 2018) and re-sampling (Hoyer et al., 2022a; Araslanov & Roth, 2021; He et al., 2008; 2021; Guan et al., 2022). To compare these methods, we take self-training as the baseline method in Fig.2 and implement re- ve-weighting (Cui et al., 2019) and re-sampling (Hoyer et al., 2022a) techniques, respectively, We observe the class bias through class-wise accuracy (ai).2(a)) and the frequency of pseudo-labels generated on the target domain during train- ing (Fig.2(b)). Loss re-weighting aims to assign different weights to classes, making the model pay more attention to tail classes. Although intuitive, the update frequency of each class to the network still varies greatly, with some classes remaining challenging to learn effectively, resulting in unstable performance in self self-training. In contrast, sam- ple re-sampling proves more effective by directly adjusting the class sample distribution during training, significantly enhancing the performance of tail classes. Despite their em- pirical solid performance, these methods are heuristic and rely on the assumption that the test and training data share the same distribution in both data and label space. However, in the UDA setting, these assumptions are invalid because (I) the class distributions of the source and target domains differ, and the target domain's 's prior class distribution is un- available; (2) the data distributions also differ, leading to varying transfer difficulties across classes in cross-domain settings. This raises the question: How to assess and alle- viate class bias directly without requiring prior knowledge about the distribution shift between the two domains?
Our contributions can be summarized as follows: (1) Prob- lem Identification (Why): We provide a comprehensive analysis of class bias in UDA settings, revealing that it is jointly affected by distribution shifts in both label and data spaces, making it difficult to mitigate using priors like regu- lar class-imbalanced problems. (2) Methodology Design (What): We propose a new perspective to assess and ef- fectively mitigate class bias by designing a class balanced learning strategy that estimates conrection terms from logits distribution, addressing the limitations of existing class-
In this work, we propose to assess the degree of class bias by analyzing the distribution of logits predicted by the net- work (S32.3.3.2). (c)) shows that the network exhibits
2

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where we define 4(ptt) as a quality estimate conditioned on confidence Ptj = max(gs (z?)) (xx))) for pseudo labels, which gradually strengthens with increasing accuracy of models Balanced Learning for Domain Adaptive Semantic Segmentation
imbalanced solutions. (3) Implementation (How): We im- plement our strategy through plug-and-play module with broad application potential in the UDA field. It first imple- ments distribution estimation, then applies an online logits adjustment to perceive the model's learning state, achieving class-balanced learning. (4) Extensive Experiments: We validate our approach through extensive experiments across various UDA benchmarks, tasks, and architectures, demon- strating that class bias is widespread challenge in UDA. Our method achieves consistent and significant performance gains, showcasing its effectiveness and versatility.
by over-sampling minority classes or under-sampling major- ity classes (He et al., 2008; 2021). In UDA for semantic seg- mentation, several approaches have introduced these strate- gies to alleviate class bias (Hoyer et al., 2022a; Araslanov & Roth, 2021; Li et al., 2022). However, these methods are still empirical and focus on the single-domain setting, which follows the assumptions that the test data and training data share the same distribution in both data space and label space, without considering the additional challenges posed by domain shift in UDA. In this work, we aim to access class bias directly and achieve balanced learning for each class with no prior knowledge about the distribution shift between domains.
2. Related Work
Here, we provide brief overview of the related work. For more detailed discussion, please refer to Appendix P.
3. Method
3.1. Problem Definition
2.1. Domain Adaptive Semantic Segmentation
In unsupervised domain adaptation for semantic segmen- tation, the network is simultaneously trained on labeled source domain data and unlabeled target domain data. To be specific, the source domain can be denoted as Ds = {(1) )= where x E Xs represents an image with of E Ys as the corresponding pixel-wise one-hot label covering C' classes. The target domain can be denoted as Dj = [(x]]) which shares the same label space but has access to the target label Yr.
Unsupervised domain adaptation (UDA) aims to transfer se- mantic knowledge from labeled source domains to unlabeled target domains, and is crucial for semantic segmentation to avoid laborious pixel-wise annotation in new target scenar- ios. Recent UDA approaches for semantic segmentation can be categorized into two main paradigms: adversarial training-based methods (Toldo et al., 2020; Tsai et al., 2018; Chen al., 2018; Ganin & Lempitsky, 2015; Hong et al., 2018; Long al., 2015b) and self-training-basse methods (Tranheden et al., 2021: Hoyer et al., 2022a; Araslanov & Roth, 2021: Zhang al., 2021). Adversarial training- based methods learn domain-invarian representations through a min-max optimization game, where a feature extractor is trained to confuse a domain discriminator, aligning feature distributions across domains. Self-training- based methods, which have come to dominate the field due to the domain- robustness of Transformers (Bhojanapalli et al., 2021), gen- erate pseudo labels for target images based on teacher- student optimization framework. However, due to the in- herent class imbalance and distribution shift in both data and label space between domains, networks often produce complicated class bias, which is further exacerbated by con- firmation bias in the self-training paradigm. Our method focuses balanced learning to address these unique chal- lenges in UDA training.
3.2. Revisiting Self-training in UDA
training- pipelines for UDA segmentation consist of a supervised branch for the source domain and unsu- pervised branch for the target domain. For the supervised branch, loss L can only be calculated on the source domain to train a neural network fo:
N₂ N
H HxW Σ
L'
(oo(f((;)) HW
(I)
where for denotes the cross-entropy loss. Unsupervised branch introduces teacher-student framework to generate
pseudo-labels 1/ argmax (go(x (aff)) with the teacher
model gΦ for target domain:
NT
RxW Σ (f 15)
L"
(2)
NT
HW
2.2. Class-Imballnces Learning
Class imbalance is common problem in semantic seg- mentation, where the number of samples per class varies significantly. Existing methods address this issue through re-weighting or sampling techniques. Re- Re-weighting meth- ods assign different weights to classes during training, giv- ing higher importance to under-represented classes (Cui et al., 2019; Lin al., 2017: Cao et al., 2019). Re-sampling techniques modify the class distribution in the training data
and can be implemented with threshold filtering or a weight- ing function. After each training step, the teacher model Is is updated with the exponentially moving average of the weights of fe. Then, the overall objective function is a combination of supervised loss and unsupervised loss as L = L. + L"
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Balanced Learning for Domain Adaptive Semantic Segmentation
3.3. Balanced Learning for Domain Adaptive Semantic Segmentation
bose
troweightng traxmpling
100
slgn
3.3.1. OVERVIEW
In this section, we first propose assess the network's 's pre- diction bias towards each class by statistically analyzing the distribution of logits (Sec.3.3.2). Based on the above analysis, we define a post-hoc method to balance the net- work's predictions (Sec.3.3.3). Furthermore, we introduce online logits adjustment tailored for the UDA training pro- cess (Sec.3.3.4) 4). Finally, we introduce cumulative density estimation as domain-shared knowledge to bridge the two domains (S.5.3.3.5).
Figure 3: Illustration of proposed post-hoc class balancing. (a) The logits distributions of over- wer-predieted and under- predicted classes. (b) Reweighting/resamplngg strategies alleviate class imbalance by adjusting the training emphasis on different classes. (c) Our post-hoc logits adjustment method aligns the logits distributions of all classes with anchor distributions to achieve balanced prediction.
3.3.2. ASSESSING PREDICTION BIAS FROM LOGITS DISTRIBUTION
probability in Eq.4 by comparing the logit values:
Given a label space y = [C] = (1.2...,CC) C) the segmen- tation network can be seen as a scorer ʃo : Zij - RC that assigns class-wise scores, also known as logits, to a pixel By from image z₁. To investigate the distribution of logits obtained by the network for different classes, we can lyze it from the perspective of the confusion matrix. The confusion matrix is a C' x C' matrix M, where each element Mat represents the number of pixels with ground truth label e predicted as class I. We replace each element Ma in the confusion matrix M with the corresponding set of logits, i.e. the logits predicted for class for all pixels with ground truth label c, obtain the logits set matrix M. We then use M to assess prediction bias.
P(Ic)) =0 [ Pa(z) II ( Poy(()))) dz. (5) s'
Combining Eq.4 and Eq.5, for an unbiased network, i.e., Bias(() = for all / E [C], a sufficient condition is that they have the same positive and negative distributions. This means the network's prediction performance is consistent across all classes. Fig. (d) shows direct correlation be- tween logit distribution differences and class bias, indicating that variations in logit distributions lead to class bias in the network's 's predictions.
Definition 1. Element in logits set matrix, Mas
3.3.3. POST-Hoc Hoc CLASS BALANCING
Ma [fo(>))) Viy = c],
Generally, the network tends to produce larger logits for over-predicted classes and smaller logits for under-predicted classes, shown in Fig.3(a). Reweighting/resamping strategies can alleviate this gap by making the network pay more attention to tail classes during training, as illustrated in Fig.3(b). However, as shown in 1, class bias does not fully correlate with the inherent class imbalance problem. especially in the UDA setting, where different distribution shifts exist in both data and label space between domains. Furthermore, these methods empirical and lack general- ization capability across various scenarios.
where (a( [[] represents the logit value predicted by the network for class I of pixel Zij. and Biy is the ground truth. In the resulting C' x C matrix M, diagonal elements Mu represent the "positive logits distribution" for class I, while off-diagonal elements Ma (c # 1) represent the "negative logits distribution" for class with respect to class c.
Definition 2. Bias of the network towards class I, Bias(I).
1 Bias(() c eEIC] e'E[c]
Σ
P(arg max fo(x)]c]] f1y- = c) C (4)
Based on the above analysis, to balance the network's pre- diction capabilities across classes, we adjust the network's predictions in a post-hoc manner. Specifically, we define an anchor distribution P, for the positive logits distribution and an anchor distribution P. for the negative logits distribution. We then align all the logits distributions with corresponding anchor distributions, as shown in Fig.3(c). To preserve the relative ordering of logits, i.c., structural information within each distribution, we align them a point-wiss wise way via the cumulative distribution function (CDF). Let Fa(z). (2(2) and (z) be the CDFs of Pat. Pp and P... respectively. We
where Parrg maxe fo())[c] 1ly = c) represents the probability that the network predicts sample from class as class l. This definition measures the average differ- ence between the probability of predieting class 1 and the uniform probability I/C across all classes. A positive bias indicates over-oredeeecoo prer-prediction, while negative bias indicates under-prediction for class I.
Let Pa denote the distribution of Ma. Assuming each dis- tribution Pa is independent, we can estimate the prediction

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Balanced Learning for Domain Adaptive Semantic Segmentation
align the logit value from Pa to Pp or Pa follows:
distributions. Considering that the source and target do- mains have inherently different logits distributions, we main- tain two sets of GMMs separately, with each set containing C x C' x K Gaussian components, where C' denotes the number of classes and K denotes the number of Gaussian components per element in M. Formally, we define:
(Fa(z)), ifc=l (Fa(z)), if
(6)
where z' is the aligned value of z with respect to the anchor distribution. For brevity, we define an offset for logit as (=) = z' - z. Considering the probability estimate that P(u; = e x exp(Ja (2x;) [e])), we can obtain revised prediction results for each pixel Tij by:
Pa a TaN dde), Pd = Σ VaiNNHHle Takk):
(8) where Pá and Pf represent the estimated logits distribu- tions for class I when the ground truth label or pseudo label is c in the source and target domains, respectively. The parameters T Hus o.x and Telk+ M d'As denote the mixing coefficient, mean, and standard deviation of the k- th Gaussian component in the corresponding GMM. We update the GMM parameters during each training iteration using the logits obtained from the current mini-bat -atch via the Expectation-Maxinne Maximization (EM) algorithm (McLacclan & Krishnan, 2007). Since the network fo gradually evolves during training, we adopt a momentum bbased EM update strategy. Specifically, we directly use the GMM parameters dei estimated in the latest iteration as the initialization ( for the current iteration. After TT EM loops, the current iteration is completed, and a momentum update is adapted where n represents the number of iterations that have not been updated since the last parameter update for Φdt We also implement GMMs to estimate the anchor distributions Pp and P., using the source domain data's global positive and negative logits. The algorithm flow is detailed in Appendix D.
exp(fo(e;)(d) + (A. <E[C] Eeeetoy eaaceexx([oo (xp(Jo(()))c)) + 7r (o((m))))))
Fy = arg max
(7) where T is a scaling factor (the derivation of Eq.7 is detailed in Appendix A). When r = 1, the model produces balanced predictions. However, to achieve optimal performance on specific evaluation metrics (e.g., mloU), we need to adjust the value of r. We discuss the choice of r in detail in the experimental section. In this way, the network generates balanced predictions for different classes.
Discussion about anchor distribution. In our experiments, we use the global positive and negative logits distributions on the source domain to estimate the anchor distribution for both source and target domains. This choice is based on two key considerations: (1) The network tends to produce larger logits for over-predicted classes and smaller logits for under- predicted classes. By using the global logits distribution, we can effectively measure the average learning degree of the network across all classes. Aligning each class-specific dis- tribution with this global distribution can help neutralize the class bias of the network, ensuring a more balanced learning process. (2) When estimating the logits distributions for each class, there exist varying degrees of statistical errors. According to Bernstein inequalities, estimating the global logits distribution can reduce estimation errors to a certain extent and accelerate convergence rates. In our case, the global distribution, being more robust and stable, serves as reliable anchor distribution for subsequent alignment. We provide further analysis and discussion in Appednxi J.
with ( <- (1 - (e + +
After estimating the logits distributions using GMMs, we compute the adjusted logits offset A and A for source and target domains, respectively. These offsets are then used to adjust the cross-entropy loss in Eq.1 and for both domains to abtatin L and L":
exp(so(=3) - - $
Th log
EE exp(so(=x) - A 3 (co(x))))))
exp(fo(x]] 1671 - - bg w (fo 10ED)
3.3.4. ONLINE LOGITS ADJUSTMENT FOR UDA
= log EC, expesp(flaaf) - AT
Sr (f(((T)))))
While the above post-hoc logits adjustment method can effectively balance the network predictions across different classes, it is performed after training the model. In the UDA setting, it is significant to incorporate the logits balancing mechanism directly into the training process. By doing so, the model can learn to make more balanced predictions while adapting to the target domain through pseudo labels.
(9)
In contrast to Eq.7, where the logits are adjusted post-hoc, Eq.9 directly incorporates the offist into the learning pro- cess of the logits. This approach is equivalent to learning scorer of the form ((x)[y) = f())[y] - (x) Con- sequently, we have argmax (x) = (<))(o] (1)) which can be seen as analogous to the post-hoc adjustment. In Appendix B, we also demonstrate that Eq.9 can be seen as an adaptive margin- based loss. By employing these ad- justed losses, we achieve a two-fold benefit: (1) The anchor distribution can serve as reference distribution to balance
To achieve this, we propose an online logit adjustment method tailored for UDA training. The key to this method lies in the online estimation of the logits distributions. We employ Gaussian Mixture Models (GMMs) to model these
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Balanced Learning for Domain Adaptive Semantic Segmentation
the learning progress between classes within both domains. (2) Since the pseudo-label-based loss in the target domain has gradually increasing weight, using a shared anchor distribution allows the logits distribution of the target do- main to gradually align with that of the source domain, thus establishing a connection between the two domains.
volve transferring knowledge from a synthetic domain to a real domain in street scene setting. Specifically, use GTAw/SYNTHIA (Ros et al., 2016; Richter et al., 2016) as the labeled source domain and Cityscapes (Cordts et al., 2016) as the unlabeled target domain, GTAv contains 24,966 synthetic images with a resolution of 1914 x 1052, while SYNTHIA consists of 9,400 synthetic images with a resolu- tion of 1280 960.
3.3.5. BRIDGING DOMAINS THROUGH CUMULATIVE DENSITY ESTIMATION
Implementation Details. Our method can be built with different wwt--traiiinn--aase frameworks. For thorough eval- uation, we apply BLDA to four strong baseline methods, i.c., DAFormer (Hoyer et al., 2022a), CDAC (Wang et al., 2023), HRDA (Hoyer et al., 2022b), and MIC (Hoyer et al., 2023), with MiT-B5 (Xie et al., 2021) pretrained on ImageNet- I (Deng et al., 2009) as the backbone. We also implement CNN-based (He et al., 2016) backbone with DACS (Tran- heden et al., 2021), and DAFormer(C). BLDA is imple- mented based on MMSegmentation (Contributors, 2020). All experiments are trained for 40K iterations and a batch size of 2, with one or two RTX-3090 (24 GB memory) GPUs, depending on the complexity of used UDA frame- works. We train the network with an AdamW optimizer with learning rates of 6 10-5 for the encoder and 6 10-1 for the decoder, weight decay of 0.01, and linear learning rate warm-up for the first 1.5K iterations. The input images are rescaled and randomly cropped to 512 x 512 following the same data augmentation in DAFormer (Hoyer et al., 2022a), and the EMA coefficient for updating the teacher net is set to be 0.999. We set temperature coefficient T = 0.1 and loss weight À 0.2 respectively.
Furthermore, for each sample pixel, sponding positive cumulative distribution value Fee based on its label c, which ranges from 0 to 1. The positive dis- tribution measures the discriminative ability of a class, and we find that this cumulative distribution value indicates the difficulty of the sample pixel belonging to that class. This structural knowledge depends only on the context of the pixel and is not affected by the image style, making it Jomain-invariant To further bridge the two domains, we add an extra regression head to the network to predict this value as an additional auxiliary task.
query the corre-
Specifically, for each sample point =i in the source do- main, we can query the corresponding positive cumula- tive distribution value based on its true label vij: di Fai, v (ff) (15)), where Fyg oe is the positive cumu- lative distribution function for class viy- Similarly, for each sample point z in the target domain, we can query the corresponding positive cumulative distribution value based on its pseudo label off: di = Fog, (Jo (1()) where F "er is the positive cumulative distribution function for the class corresponding to the pseudo label oTy. We then add an extra regression head hs to the network to predict the cumulative distribution value for each sample point. The corresponding regression losses for the source and target domains be defined as:
4.2. Comparison with Existing Methods
Comparative Evaluation. We compare BLDA to existing state-of-the-art UDA approaches on the GTA.-CS and -YN. benchmarks. In addition to the widely adopted mean intersection-over-uiion (mloU) metric, we also report the mean accuracy (mAcc) metric, which is equivalent to measuring the balanced error (Menon et al., 2020), i.e., the average of each class's error rate, and is more suitable to assess the balance among classes. We discuss these two metrics in detail in Appendix C. Additionally, we calculate the standard deviation of IoU and Ace for each class to reflect the balanced degree of performance across classes.
Ns Hxw Σ
Ling
1
Σ
(no(f((()))) - =d
Ns
(10)
NT HxW
Σ
((pss) (pss))lol (f d:),
Freg
NT
where 12 denotes the L2 loss, and (Z.5)) denote the features extracted by the network fo. Finally, the overall training objective can be expressed L L + L" X( + CTeg). where À is hyperparameter balancing the cumulative density estimation loss.
Tab. shows BLDA consistently improves the performance of all baseline methods on two benchmarks by a large margin, ranging from 1.2% to 3.1 1%. Furthermore, significant improvements are obtained for under-predicted classes, such as sidewalk, pole, light, and sign, which demonstrates that BLDA can mitigate the class bias with decreased standard deviation and thus bring the performance gains. In Table 3, the same phenomenon is observed, and the improvement in MACe is more significant,
Evaluation Results.
4. Experiments
4.1. Experimental Setup
Datasets. Following standard UDA protocols, we evalu- ate our method on two widely used benchmarks that in-
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Balanced Learning for Domain Adaptive Semantic Segmentation
Table 1:
UDA segmentation performance on GTA.-XS using the mloU (%) evaluation metric, where the improvement is marked as bold. The results are acquired based on CNN-based model (He et al., 2016; Chen et al., 2017a), denoted as C, and Transformer-based model (Xie al., 2021), denoted as T. denotes the reproduced result.
Silewall
Building
Wall
Pule
Light
P55s0
Brock
Bus
Motoe
Bike
(T)
(1))
Sog 2018)
$6.5
36.0
79.9
23.4
23.3
23.9
352
14.8
83.4
33.3
15.6
5K.5
27.6
73.7
52.5
35.4
3.9
30.1
25.1
42.4
24.7
CRST (Zon sl. 2019)
91.7
45.1
80.9
29.0
23.4
43.8
47.1
40.9
84.0
20.0
60.6
64.0
31.9
85.8
59.5
48.7
25.0
38.0
47.0
49
21.6
(Kang at.
ProDA (Zhung et al. 2021)
87.8
56.0
79.7
66.3
44.8
45.6
53.5
53.5
88.6
45.2
70.7
39.2
58.8
45.5
50.4
48.3
56.4
ST.5
21.2
CPSL. (13 al., 2022)
92.3
99.5
84.9
45.7
70.1
52.8
61.5
59.5
87.9
41.6
85.0
75.0
35.5
90.4
48.
75.9
26.3
53.8
53.9
50
20.3
TaasDA (Chen 2022)
94.7
64.2
89.2
45.8
50.1
60.2
40.5
90.4
50.2
76.7
47.6
92.5
56.8
60.1
49.6
55.4
63.9
18.5
Tudknvvic.
DIGA (Shen 2023)
97.0
91.3
60.8
56.5
64.4
69.9
91.5
50.8
93.7
79.3
55.2
93.7
86.0
77.8
65.8
74.
14.7
CoPT (Mata et all, 2025)
97.6
50.9
91.6
62.1
55.9
59.3
66.7
70.5
91.9
53.0
94.4
80.0
55.6
94.7
87.1
68.5
82.1
65.0
63.8
76.1
14.7
(Immhheden
87.5
87.9
67.8
19.4
87.1
41.2
87.3
45.5
45.3
36.3
25.6
DAFormer())" (Hoyer et al., 2022a)
95.7
09.9
87.2
35.6
36.7
57.00
40.4
52.8
87.3
44.1
K7.9
69.0
42.2
86.5
40.0
51.7
0.2
41.1
54.0
56.
24.0
+BLDA
95.6
7366
86.7
41.0
40.2
43.3
51.1
62.4
86.2
43.7
87.4
68.3
39.2
86.6
4336
45.5
1,0
49.4
59.4
S8.1
23.2
(Hayer
16.8
+BLDA
55.7
89.2
47.3
71.4
CDAC (Rlng al. 2023)
96.1
72.8
90.5
55.2
48.0
51.8
57.1
61.8
90.8
50.4
91.9
75.2
46.9
93.6
80.9
78.6
58.2
56.9
64.5
69
16.7
+BLDA
96.6
78.1
900
57.9
52.5
55.1
SUL7
64.5
90.1
50.8
9019
73.3
47.5
93.2
75.
N0.0
65.3
60.7
68.9
71.8
15.4
20226)
15.4
-BLDA
77.6
90.7
62.1
91.3
52.
76.9
57.3
87.7
79.9
68.9
13.8
MIC (Hoyer et al., 2023) .BLDA
97.4
80.1
91.7
61.2
56.9
52.7
66.0
71.3
91.7
$1.4
94.3
79.8
56.1
94.6
85.4
90.3
80.4
64.5
68.5
75.9
14.8
97.1
826
91.6
64.7
61.0
64.9
68.0
74,8
91.2
56.6
92.4
80.0
54.7
95.7
87.3
88.8
82.6
64.2
72.0
77.1
13.5
DAFormer -post-hor
DAFormer -BLDA
Ground Truth
Image
DAFormer
CDE
Figure 4: Qualitative results. Note that the yellow boxes mark regions improved by BLDA.
TraffleSSgn
Hagdde
70.5
mikec
Veytotion
002
0.30
2.50
facton(s)
factoris)
Figure 6: Comparison Logits Distribution. We choose (building (2), vegetation (8)} as over-predicted classes, and (traffic sign (7), bicycle (18)) under-predicted classes for visualization. Note that the anchor distribution is counted separately at baseline and in our method.
Figure 5: Study of the different evalution metrics with re- spect to scaling factor
ranging from 2.9% to 5.6%.
4.3. Ablation Study
Influence of scaling factor. As illustrated in Fig. 5, we explore the influence of different 7 values on evaluation metrics. The mAcc metric gradually increases as grows, reaching its peak performance when = 1. Interestingly, the mloU metric does not demonstrate a perfectly positive correlation with the rise in mAce. This discrepancy arises
We conduct series of ablation studies on the GTA.->S. benchmark built with DAFormer (Hoyer et al., 2022a) in this section. Please refer to the Appendix E-O for further analysis, where we provide more experiment results, deeper ablation studies, and more visualization.

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Balanced Learning for Domain Adaptive Semantic Segmentation
Table 2: UDA segmentation performance on SYN.-cS. using the mloU (%) evaluation metric, where the improvement is marked as bold. Note that the mloUs on are calculated over 16 classes.
Sidrwalk
Building
Roud
Wall
Ponie
Light
Sign
Tomsin
Sky
Ridee
Train
Minor
Method
Arch.
mixel (1)
sid (L)
CRST (Zou et ol. 2019)
C
67.7
52.2
73.9
10.7
1.6
37.4
22.2
31.2
80.8
50.5
60.
29.1
82.8
25.0
19.4
45.3
43.8
26.0
PLCA (Kong
82.6
K2.3
62.1
46.8
28.3
DACS (Iranhedea
2021)
80.6
25.1
21.5
2.9
22.7
903
82.9
48,3
27.5
ProDA (Zhang et al., 2021)
C
87.8
45.7
84.6
37.1
0.6
44.0
54.6
37.0
88.1
84.4
74.2
24.3
88.2
51.1
40.5
45.6
55.5
25.6
CPSL (Li et al., 2022)
87.2
43.9
85.5
33.6
0.5
47.7
57.4
57.2
87.8
53.5
794
32.0
90.6
49.4
50.5
59.8
57.9
25.5
TOnADA (Ches
90.4
44.4
ADFormer Todorovie, 2025)
91.8
53.6
40.5
5.2
54.9
92.6
86.1
50.
62.1
22.9
DMGA (Shen et al., 2025)
I
85.2
47.7
88.3
49.5
4.8
57.2
68.7
60.9
85.5
92.9
79.4
52.8
89.0
64.7
63.9
64.9
65.8
21.4
CaPT (Mata et al, 2025)
T
83.4
44.3
9010
50.4
8.0
60.0
67.0
63.0
87.5
9488
81.1
58.6
89.7
66.5
68.9
65.0
67.4
21.1
(Iluyer et 2022a)
40.7
59.8
57.2
22.1
-BLDA
I
80.7
44.9
K5.6
45.1
9.6
54.3
60.2
58.7
87.7
91.3
75.7
51.1
87.3
62.7
59.9
65.8
64.0
20.6
HRDA (Blover et al. 2022b)
T
85.2
47.7
88.3
69.5
4.8
57.2
65.7
60.9
85.3
92.9
79.4
52.8
89.0
61.7
63.9
64..9
65.8
21.4
+BLDA
T
83.9
54,9
67.5
53.1
11.5
69.4
64.4
87.2
93.1
79.1
54.7
88.3
69.1
64.2
65.7
67.9
19.3
MIC (Hoyer et al., 2023)
1
86.6
50.5
47.
7.8
87.1
94.6
90.1
67.3
21.0
-BLBA
T
86.1
61.2
89.8
47.2
10.2
62.6
70.3
67.1
90.0
94.1
81.4
56.4
90.5
67.2
64.8
66.3
69.1
20.4
Table 3: UDA segmentation performance on GTA. using the mAce evaluation metric, where the improvement is marked as bold. * denotes the reproduced result.
Salevoit
Pd0c
Light
Tomain
Parsen
Trnck
Monu
Method
mAke (1)
sidl (L)
DACS
(Iranheten et al. 2021)
97.9
38.9
$4.99
20.7
19.4
16.1
51.7
57.2
94.5
06.9
95.6
R2.0
62.2
93.3
82.9
79.0
0.0
74,4
2000
65.3
26.5
+BLDA
97.1
80.5
93.4
54.1
39.2
50.5
64.6
67.4
93.7
71,5
99.0
83.4
38.8
95.0
73.0
76.2
1.0
73.5
39.2
69.0
24.2
DAFormerC)" (Hoyer ot
93.6
45.0
93.8
69.2
77.0
85.3
60.8
23.8
+BLDA
97.0
84.8
93.0
55,1
56.1
55.4
71.0
74.5
93.2
73.7
97.7
84.8
77.2
92.0
90.1
85.5
1.1
77.3
73.0
74.9
21.6
DAFormer (Hoyer et al, 2022a)
99.1
74,8
95.2
61.0
53.1
59.8
7037
68.6
96.0
63.4
98..7
84.1
69.9
955.5
38.9
84.1
74,0
70.0
09.7
77.8
14.2
+BLDA
98.3
86.7
93.4
66.0
74.0
78.9
95.4
85.7
73.1
89.8
89.3
77.2
78.6
11.9
CDAC (Wang 2023)
94.6
95.8
74.7
13.8
+BLDA
98.1
82.5
94.0
74,8
66.7
68.0
76.8
74.1
95.5
67.9
98.3
87.1
72.9
95.6
98.8
88.8
71.8
80.8
$1.9
81.5
11.5
HRDA (Hoyer et 2022b) +BLDA
99.1
33.6
96.0
72.5
56.3
69.4
83.9
79.6
96.1
39.1
98.5
89.8
60.7
96.2
91.3
89.4
80.2
77.0
50.9
82.2
13.2
99.2
87.2
95.0
61.9
68.2
72.7
88,3
79.5
95.7
65.7
87.9
79.6
95.5
93.8
92.5
87.0
83.1
51.2
85.1
10.7
(HCyer et 2025)
96.0
08.5
96.8
58.9
85.7
81.5
96.7
91.4
91.0
78.1
79.0
77.5
11.6
+BLDA
98.9
90.7
95.4
74,6
70.9
73.5
$8.9
82.8
96.4
64.8
98.8
89.0
80.8
96.3
93.8
92.8
88.2
86.5
78.8
86.3
9.8
Table 4: BLDA ablation study of different components.
performance boost of 1. 1.9%, suggesting that this strategy ef- fectively captures the disparity in learning degrees between the domains. Lastly, the extra supervison from cumulative distribution estimation (CDE) further models the shared structural information across the domains (shown in Fig.4), producing additional performance gains.
None Post-hoc OLAs OLA CDE
mloU mAcc
68.3
77.8
69.2
80.3
68.9
79.5
70.2
81.8
Qualitative Results. Fig.4 presents the qualitative results. We observe that for under-predicted classes, such as side- walk and pole, the baseline method struggles recognize them accurately. While the post-hoc method can slightly improve the performance, significantly enhances the ability to predict these classes.
70.7
82.0
from the fact that the mIoU calculation is heavily impacted by the imbalanced distribution of the test set, whereas mAcc serves class-balanced metric. We comprehensively plain this phenomenon in Appendix C. Our method, which models class- balance learning, effectively boosts mAcc. However, to achieve improvements in mloU, selecting a smaller scaling factor 7 is necessary.
proposed BLDA approach
Comparison of Logits Distribution. In Fig.6, we vi- sualize the positive distribution and negative distribution corresponding to the over-predicted classes {building (2), vegetation (8)) and prediitdd classes {traffic sign (7), bicycle (18)) on the Cityscapes Val. set. In the baseline method, the positive and negative logits of classes building and vegetation are larger than anchor distribution, while this phenomenon reversed in classes traffic light and bicycle, which leads to class bias. Our method reduces this distribu- tion difference by aligning with the anchor distribution and achieves Mlassbbllance learning.
Effectiveness of Components. In Tab.4, we delve into the various components of BLDA. By solely applying the post-hoe method to adjust the predictions, observe mi- nor performance improvement of 0.9%. When introducing online logit adjustment exclusively during source domain image training (OLAs), the improvement is comparatively modest at 0.6%. However, by simultancously performing ad- justments in both domains (OLA), we witness a significant
8

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Balanced Learning for Domain Adaptive Semantic Segmentation
5. Conclusion
connected erfs. JEEE transactions on pallern analysis and machine intelligence, 40(4))834- 848, 2017a.
In this work, we present Balanced Learning for Domain Adaptation (BLDA), novel approach address class bias in unsupervised domain adaptation (UDA) for semantic seg- mentation. BLDA analyzes logits distributions to assess prediction bias and introduces an online logits adjustment mechanism to balance class learning in both source and target domains. Our method effectively mitigates class bias, promotes balanced learning, and enhances generalization to the target domain. Experimental results demonstrate con- sistent performance improvements when integrating BLDA with various methods on standard UDA benchmarks. The extensive experiments across different tasks, basclines, and architectures showcase the effectiveness and versatility of BLDA in addressing class bias in UDA settings.
Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. Rethinking atrous convolution for semantic image seg- mentation. arXiv preprint vrXiv:776600588,, 2017b.
Chen, M., Xue, H., and Cai, D. Domain adaptation for semantic segmentation with maximum squares loss. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. 2090-2099, 2019.
Chen, R., Rong, Y., Guo, S., Han, J., Sun, F,, Xu, T. and Huang, W. Smoothing matters: Momentum trans- former for domain adaptive semantic segmentation. arXiv preprint arXiv:220..07888, 2022.
Chen, Y., Li, W., and Van Gool, L. Road: Reality oriented adaptation for semantic segmentation of urban scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7892-7901, 2018
Acknowledgements
This work was partially supported by the Open Fund of National Key Laboratory of Deep Space Exploration (Grant NKLDSE2023A009),
Chen, Y., Li, W., Li. Z., Sun, R., Zhang, T., Xiong, Z., and Wu, F. Sam-glomeruli: Enhanced segment anything model for precise glomeruli segmentation. In Interna- tional Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis, pp. 182-191. Springer, 2024.
Impact Statement
Within this paper, we present an approach for domain adap- tive semantic segmentation, pivotal research area in the realm of computer vision, with apparent negative societal implications known thus far.
Chen, Y., Sun, R., Li, W., Mai. H.. Luo, N., Pan, Y., and Zhang. T. Alleviate and mining: Rethinking unsupervised domain adaptation for mitochondria segmentation from pseudo-label perspective. In Proceedings of the AAAI Conference on Artificial Intelligence, 2025.
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Balanced Learning for Domain Adaptive Semantic Segmentation
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12

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Balanced Learning for Domain Adaptive Semantic Segmentation
A. Derivation of Eq.7
Considering probabilities estimate that p(un - clai)) x exp(fo(z;;)le)) the discriminant probability for pixel Ziÿ can be presented as:
exp(Ja(ro)(
p(u. = d(wi)) = Ee exp( (xp(fo(m)))e))
(11)
Given offset for logits as (=(=)) = x' - 2 and the label с for pixel Tij. we can obtain revised class-conditional discriminant probability for each pixel zij by:
exp(fo(asg)e) + Aoo Ply cless c) Ee exp(fo(Fu)) And (Jo(av))e)))
(12)
Then, following Bayes rule, the revised posterior is derived as:
P(ys c/2t,)) p(c)p(yy c) c)
(13)
p(c))puu clas,, c)
So can obtain revised prediciton results for each pixel zij by:
おi =
argmax = c(ois,
(14)
Since the class probabilities p(c) are typically set as a uniform prior, i.e, p(c) = y. Eq. 14 can rewritten as:
Tyy argmax.e:c c)
exp(fo(z:))(e + (cc(f((z))) argmax.e (xpp(fooeepppp((xx (fa Ee exp(fo (z,(fo(z)))ee (c,)) And (fo(s))eee))
(15)
For Eq.6, we add a scaling factor to be adjusted for specific evaluation metrics.
B. Discussion about Eq.9
For deeper understanding of the loss function in Eq.9, we can rewrite it as:
exp
A w
(fo
(=(((())
log 1+1
Σ
exp -o(e))l)-o(())llll)
(16)
No
exp
A n
(fo
which can be interpreted as standard cross-entropy loss with adaptive margin. Specifically, if the class vi is an over-predicted class, it is reasonable to assume that for most other classes c, A S Mi mi, (fo(=))) < AS og (f This implies that:
exp
AA Mi,
(fo(=
M
<1
(17)
exp
AS
(Jo (Jo(x
In the loss function Eq.16, 16, this ratio is used to scale the term exp (fo (fo(z)) ld - fo (vgl)) When vi is an over-predicted class, the ratio is less than which reduces the weight of the term exp (fo(z:)) (d fo(=;)) (ull)) Consequently, for over- predicted classes, the loss function imposes smaller penalty for misclassification. Conversely, if y an under-predicted Agg Ag (fo [e]) This leads to a ratio
class, it can be assumed that for most other classes c, AS og (=
greater than 1, which increases the weight of the term exp (=(f=))) (=;5))) thereby imposing a larger penalty for misclassification of under-predicted classes. In summary, by introducing the margin-based ratio term, the loss function 16 adaptively adjusts the strength of the penalty based on the difficulty of the classes. This approach helps to mitigate the class bias problem and enhances the model's performance on under- predicede classes, leading to a more balanced and accurate classification. Moreover, since the logit offset term is updated online during the training process, it aligns well with the self-training paradigm in UDA.
13

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Balanced Learning for Domain Adaptive Semantic Segmentation
C. Discussion about Evaluation Metrics
In this section, we investigate the impact of class imbalance on the evaluation metrics mloU and mAce in the context of semantic segmentation. We consider a multi-class problem with C classes, where the number of samples in the i-th class denoted as N₁. Let P.y represent the probability of classifying a sample from the i-th class as the j-th class in the confusion matrix. For the purpose of this analysis, we assume that the probabilities Pa and Pkk are balanced across all classes, i.e., Pa = PRR = p, Vi, k 1, 2, ..., C, and focus solely on the effect of class imbalance in terms of sample numbers. Under this assumption, the calculation formula for mAcc can be written as:
C
1 3 m.Acc =
P. N, Psj
1
Σ =1 1== Ps
P
- C
(18)
C
1=1 E
As evident from the equation above, the sample numbers N, cancel out in the calculation of mAce, making it independent of the class imbalance in terms of sample numbers. Therefore, mAce remains unaffected by class imbalance under the given assumptions. On the other band, the calculation formula for mloU is given by:
1 C i=1 D== N. P. + Σj= N2 P/s N₁ Paa
N Pa C
mloU
(19)
In contrast to mAcc, the sample numbers N; do not cancel out in the calculation of mIoU, Consequently, when the classes are imbalanced, i..,, the sample numbers N, vary significantly across classes, the IoU of classes with larger sample numbers will dominate the overall mloU result. To illustrate the impact of class imbalance on mloU, let us consider case where class k is head class with a significantly larger sample number N& compared a tail class i with sample number N,. The performance of class i will be greatly affected by class A through the term No Pas in the denominator of mloU. For class i to have fair contribution to mloU, the probability Pki needs to be very small. This implies that a balanced classifier may not be optimal for maximizing mIoU under class imbalance. Therefore, our proposed method implements a scaling factor to modulate the contributions of introduced logits offset. In contrast, mAce is inherently unaffected by class imbalance, as the sample numbers N, cancel out in its calculation formula. This means that a balanced classifier is indeed optimal for maximizing mAce, and our method, which aims to balance the contributions of different classes, aligns well with this objective and can achieve consistent improvements.
D. Implementation Details of Online Logits Distribution Estimation
In this section, we provide the pseudo label to explain implementation details of online logits distribution estimation, as shown in 1l For computational efficiency, in each iteration, we sample Nsample logits for each element in M and MT. where Naample is the minimum sample number of classes in the minibatch (shoule be greater than or equal to Nmmis where we set as 100). This way. we obtain C x C x Nsample logits, and then update the C x C GMMs simultancously in a parallel manner. Since not all classes may be updated in each iteration, we maintain variable n for each GMM that not updated, to record the number of iterations since its last update. This is used to adjust the momentum factor using 7 in the EMA update, in order to match the update speed of the network.
In the algorithm, the cumulative distribution functions (CDFs) FS. FT, Fg. and Fs are computed using the estimated Gaussian mixture models (GMMs). These CDFs describe the cumulative probability distribution of the corresponding GMMs.
For the source and target domain GMMs P and PT, their CDFs can be represented as: F)(2) = Σk= T k D M-MLL LLk
(2) Dk . @ - He..), ), where o(-) is the CDF of the standard normal distribution, and = H and o are the weight, mean, and standard deviation of the k-th component of the source domain GMM PS, respectively. -hd,k" "T, and c are the corresponding parameters for the target domain GMM PT. Similarly, the CDFs for the anchor GMMs Pp and P., are: Fş(2) = Σ FT. $ op.k
= ΣK Ti.k $ (²=MmnA). The inverse function of a CDF, denoted as F-1(.), represents the value of the variable corresponding to given cumulative probability. For a given logit value 2, by computing 1 (P=)((()) (z)) and F (15)() (z)). we obtain the corresponding logit values of the positive anchor distribution Pp and the negative anchor distribution Pa at the cumulative probability (2). Then, the difference between these values and the original logit value is used as the logits offset A (2) for the source domain. Similarly, by computing
Fz(
k=
14

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Balanced Learning for Domain Adaptive Semantic Segmentation
Algorithm 1 Online Logits Adjustment for UDA
Require: Source domain Ds (a.?. u Ns target domain Dy (xa)
Ji number of classes C, number of Gaussian
Nr
components K, momentum factor T, scaling factor v, minimum number of elements Nmnn number of EM Loop T.
Ensure: Model parameters 0.
1: Initialize model parameters 0, source GMMs Páj target GMMs Pa anchor GMMs Po and Pn for all / E [C].
2: while not converged do
3: Sample mini-batch of source data (x.² yr
By uf) )==1 and target data (x (=1))=
Br
4: Compute logits fo()))) and fo(z))) for source and target samples.
5: Compute pseudo-labels for target samples: Dij = ar|max.e|c;/ So (x]])))
6: Compute matrices M and My based on the source labels and target pseudo-labels:
7: M = ( [7] V.y = c}
8: MI = (Jo(4t))0 [4] Di c}
9: Update source GMMs PS. target GMMs PT anchor GMMs Pe and P. using the momentum-based EM algorithm:
for c, E [C] do
11: if [M Nuin then
12:
Initialize da t 28 from the latest iteration.
13:
for t L..... T do
14:
Update Pet using the EM algorithm with current logits. end for
(t)
15:
16:
25 + (1 0a
8.())
+ F" is where n is the number of iterations since the last update.
end if
18:
if M
N Muin then TT(0)
19:
Initialize Oet
t ST from the latest iteration.
20:
for / 1,...,7 do
21:
Update o (t) end for 02 + -") T.T)
using the EM algorithm with current logits.
22:
iT + F where n is the number of iterations since the last update.
23:
24:
end if
25:
end for
Update anchor GMMs Pp and Pn. using the global positive and negative logits from the source domain:
Initialize (0) < Sp. (0) 6n from the latest iteration.
27:
28:
for = I.....T do
29:
Update /0¹² 2h using the EM algorithm with current global logits. end for
30:
6p + (1 7) 00 (T) + dn + (1 7) 7) 7
31:
32:
Compute cumulative distributions FS. FT, Ep. Fn using the estimated GMMs.
(Fd 2, ifc=
34:
Compute logits offsets for source domain: (2)
F FFd (2)) -2, ifc#
Fr (FF(()))) -2, ifc=1
35:
Compute logits offsets for target domain: (1
F, (F -2, c
36:
Compute the adjusted losses E. and L" using Eq. (8) with A and AT.
37: Update model parameters 0 by minimizing L 4 using optimizer (e.g., SGD or Adam).
38:
end while
39:
return Model parameters 0.
(FT( and F- (F (z)), we obtain the logits offset A (x) for the target domain. To efficiently compute the CDF and its inverse function, we use the Abramowitz-Ster formula to approximate the CDF in the form of a polynomial and employ interpolation methods to estimate the inverse function.
15

---

Balanced Learning for Domain Adaptive Semantic Segmentation
Table 5: Parameter study of
Table 6: Parameter study of
Table 7: Parameter study of
Table 8: Parameter study of
K.
4.
T.
K
mlou (%)
F
mlou (%)
T
mlou (%)
mlou (%)
70.2
0
68.6
70.4
0.05
70.3
70.5
0.9
70.0
3
70.7
0.2
70.7
70.7
0.99
70.7
5
70.7
0.5
70.4
10
70.6
0.999
70.3
10
70.5
69.9
E. Influence of Parameters Setting
In this section, we further study the influence parameters setting introduced in BLDA, i.c., number of Gaussian components K, momentum factor 7, number of EM Loop T for GMM estimation and for cumulative density estimation loss. All experiments are conducted with DAFormer (Hoyer et al., 2022a) on GTA
Number of Gaussian Components K. As shown in Tab.5, we find that BLDA can work well even when K 1, since the logit distribution is naturally close to Gaussian. The model achieves the best performance when K = 5. A larger K
allows for more flexibility in modeling the logits distributions but may also introduce noise. We choose K as balance between model capacity and robustness.
Momentum Factor The momentum factor controls the speed of updating the GMM parameters. When F = performance becomes erratic because the logits from the current iteration alone are not sufficient model the distribution of all logits. A larger F leads slower updates, retaining the previously estimated distribution and making the estimation more stable but less adaptive. As presented in Tab.6, setting F to 0.99 yields the best performance, suggesting that relatively stable estimation of the logits distributions is beneficial for the adaptation process.
Number of EM Loop T. The number of EM loops T determines the number of iterations used to update the GMM parameters in each training step. 7ab.7 shows that the model is not sensitive to the choice T, since the convergence rate of GMM is faster than the rate of network update, and it can be estimated well even when T 1. We choose = 3 for stable performance while considering computational efficiency.
The weight À balances the cumulative density estimation loss with the segmentation loss. A higher À enforces stronger domain alignment through the cumulative density functions. As shown in Tab.8, À = 0.2 provides the best performance gain. An overly large À may distract the model from learning the primary segmentation task, leading to performance degradation.
Cumulative Density Estimation Loss Weight A.
F. Additional Experiment Results
In Table 9, report additional Acc metric for Table The improvement with mAce in this benchmark is also more significant, which aligns with our expectations.
Table 9: UDA segmentation performance SYN.- using the mAcc (%) evaluation metric, where the improvement is marked as bold.
Wall
Aggh
Eerrai
Sky
Bus
Method
Arch
mAcc (1) (L)
DAI DAFormerl 20222)
39.8
902
96.2
33.8
51.9
63.1
37.8
95.1
95.4
86.3
63.8
95.7
B3.4
33.4
61.4
X0.7
25.1
+BLDA
HRDA (bloyer al., 20226)
90.3
85.
96.0
69.4
8.0
70.6
81.2
69.6
94.7
99.1
88.0
68.9
91.4
93.3
71.3
74.0
73.6
21.2
+BLDA
89.4
94.5
95.4
75.5
34.5
78.
85.8
74.7
97.6
98.8
88.9
71.6
97.4
96.9
72.6
76.2
82.4
17.9
MIC (Hoyer 2023)
73.0
73.4
72.5
G. Extended Experiment on Image Classification
To demonstrate the generality of BLDA, we implement BLDA based on MIC (Hoyer et al., 2023) with ResNet-100 101 on the VisDA-22 2017 (Peng et al., 2017) UDA classification benchmark in Tab. 10, and our method still achieves improvements. In the classification task, the dataset does not have severe class distribution differences like segmentation. However, as we
16

---

Balanced Learning for Domain Adaptive Semantic Segmentation
point out in Fig.l, the transfer difficulty differences between domains still lead severe class bias in this task, and method can effectively alleviate this and achieve more balanced predictions.
Table 10: Image classification accuracy in on VisDA-2017 for UDA, where the improvement is marked as bold.
Method
Plane Beyel Bus Car
Horse Knife Mcyle
Persn
Plant
Sktb
Train
Truck
Mcan (1) Std (U)
MCC (Jin et al., 2020)
88.1
80.3 80.5 71.5 90.1
93.2
85.0
71.6 89.4 73.8
85.0
36.9
78.8
14.4
SDAT (Rangwani et al., 2022)
95.8
85,5 76.9 69.0
93.5
97.4
88.5
78.2
93.1 91.6 86.3
55.3
84,3
11.9
MIC (Hoyer et al., 2023)
96.7
88.5
84.2 74.3
96.0
96.3
90.2
81.2
94.3
95.4
88.9
56.6
86.9
11.3
+BLDA
96.2
90.3
82.8
81.2
95.7
96.7
93.4
86.5
95.7
94.3
65.7
89.1
8.7
H. Extended Experiment on Video Semantic Segmentation
We further validate our method on the Domain Adaptive Vidco Semantic Segmentation setting. Table 11 shows results on VIPER (Richter et al., 2016) - Cityscapes-Seq, and Table 12 shows results on VIPER - BDD (Yu et al., 2020), where we divided the BDD 100k subset following (Kareer et al., 2024). All experiments are based on the Deeplabv2 (Chen et al., 2017a) segmentation network.
Table 11: Domain adaptive video semantic segmentation performance on VIPER - Cityscapes-Seq.
Sidowall
Light
Terni
Truck
Moter
Bike
Method
mlool (1)
sid (L)
PAT atal. 2024a)
45.3
35.7
36.1
56.6
34.8
37.9
29.3
2919
24.0
61.4
MIC (lloyer et al., 2023)
95.5
71.5
91.2
21.1
66.2
73.1
89.8
47.3
92.3
79.5
89.7
43.6
$4.9
38.5
64.6
68.3
21.7
+BLDA
95.1
73.1
90.7
29.9
69.6
74,2
89.6
48.9
90.5
77.9
89.8
50.1
56.7
40.7
69.1
69.7
19.7
Table 12: Domain adaptive video semantic segmentation performance on VIPER - BDD.
Road
Sidowall
Muilding
Light
Sign
Vigg
Tarain
Parsm
Car
Method
unloU (f)
std std(()
20226)
28.0
25.0
84.4
29.7
+BLDA
84.4
40.1
79.5
18.7
27.3
29.1
74.7
25.5
82.6
62.8
81.7
46.7
35.1
29,9
50.8
23.4
BLDA Efficiency Analysis
In our experiments, our method is highly efficient in terms of both training time and computational cost. We have provided a detailed explanation of our implementation in Appendix D. The additional cost introduced by our proposed components can be attributed to the following factors, for which we have implemented efficient solutions:
GMM implementation: Instead of using off-the-shelf libraries update the parameters sequentially, store the parameters of C' x C' x K Gaussian components as tensors in PyTo PyTorch and perform parallel updates.
CDF computation: We use the Abramowitz-Stegi formula to approximate the CDF the form of polynomial.
Inverse CDF computation: We use interpolation algorithms to approximate the inverse CDF within the estimated range of values.
All the above operations can be efficiently performed using simple matrix operations on tensors. Moreover, the storage of Gaussian component parameters and the additional regression head (a 1 conv) introduced by our method are lightweight. We also report the training time and computational cost of our method when applied different methods, shown in the Table 13. Our method is computationally efficient, with an average increase of 0.3s per iteration for each method (all methods are trained with a batch size of 2 and an image crop size of 512 x 512). The total additional time depends on the total number of iterations for each method. For DAForeer and DACS, we observe an increase of approximately 3GB in GPU memory usage, while for CDAC, no additional memory consumption is observed (possibly because our module reuses the cached memory already allocated by this method).
17

---

Balanced Learning for Domain Adaptive Semantic Segmentation
Table 13: Computational resource requirements comparison
Methods
GPU Memory (MB)
Time per iter (s)
Total Time (h)
DAFormer (Hoyer et al., 2022a)
9,807
1.32
14.5 (40K iters)
+BLDA
12,655
1.59
17.7 (40K iters)
DACS (Tranheden et al., 2021)
11,078
0.52
35.5 (250K iters)
+BLDA
14,354
0.81
56.1 (250K iters)
CDAC (Wang et al., 2023)
35,443
1.66
18.7 (40K iters)
+BLDA
35,443
1.95
22.3 (40K iters)
J. Further Discussion With Anchor Distribution
In our experiments, we use anchor distribution estimated from source domain to balance both source and target domains. Normally, there exists a discrepancy between the anchor distribution and the true distribution of the target domain. However, the impact of this distributional difference on the final performance is relatively small because the relative difference between the positive and negative distributions plays more crucial role in the anchor distribution, while the absolute difference in their overall distributions (e.g., simultancously mecreaingg/decraasiii pos/neg) does not significantly affect the performance. In Fig. Ic)), we observe that the biases of positive and negative logits are coupled, meaning they tend to be either both larger or smaller. The differences in bias distributions are more reflected in the absolute differences of the overall distribution, while the relative differences between pos/neg do not vary significantly. Intuitively, if there is distributional bias between the target domain and the source domain, this difference would also be more evident in the overall absolute difference.
For empirical results, conduct ablation study on the selection criteria for these anchor distributions, considering both post-hoc and online logits adjustment (OLA) implementations to observe the results in Table 14. In our setting, the anchor distribution is obtained from the global distribution statistics of the source domain. For the post-hoc implementation, we consider the global distribution of the target domain and the pos/neg logits distributions of two specific classes (building and fence, which are the two most biased classes Fig. (cd) as anchors. We find that the impact of different anchor distribution choices on the final performance is relatively small.
For the OLA implementation, we find that using the global logits distribution is important because our online distribution estimation approach leads to smaller estimation errors when using global pos/neg statistics, promoting stable training during the self- liff-training process.
Table 14: Ablation on different selection criteria for anchor distributions GTA. using the mloU (%) evaluation metric. OLA denotes online logits adjustment.
Rood
AVe
Truck
Jrain
Manor
Anchor
Strategy
milll (f)
(L)
DANcomer (Hoyer al. 30C2a)
70.2
49.6
59.4
89.9
722
92.3
74.5
55.9
61.8
10.5
target (global)
post-hoe
95.7
77.0
67.8
61.0
53.7
54.1
56.1
61.2
87.2
50.2
90.4
74.
43.4
96.9
73.1
82.5
56.6
54.8
68.1
60.4
15.8
(buiddnng)
post-hoc
95.8
77.1
90.0
59.8
54.5
51.6
56.6
59.8
86.2
48.2
90.6
75.4
43.7
90.4
72.0
79.5
58.9
53.7
69.2
69.1
16.0
71.7
1600
somne (plobal)
post-hoe
95.7
77.1
58.6
55.3
48.5
57.5
60.9
89.5
47.2
91.0
72.9
43.7
91.3
73.7
90.8
55.3
61.8
69.2
1622
source (buldding))
OLA
95.5
77.9
58.5
60.4
59.1
53.3
57.9
60.
88.3
49.2
90.3
76.1
43.6
90.3
76.4
85.7
56.2
57.9
68.8
70.3
15.8
(fence)
81.2
57.0
87.9
76.3
75.0
56.0
70.6
16.0
souree (global)
78.3
88.3
55.2
55.7
65.2
89.3
47.3
71.
91.6
74.3
73.2
59.3
67.1
70.7
15.5
Furthermore, our anchor distribution serves two purposes during the training process, as discussed in Sec.3.3.4: Using a shared anchor distribution allows the logits distribution of the target domain to gradually align with the source domain. In this implementation, the anchor distribution can both serve as a reference distribution to balance the learning progress between classes within domain and act as a bridge to connect the two domains at the same time.
To further illustrate this point, please refer to Appendix B. Our logits adjustment can be interpreted as a standard cross-entropy loss with an adaptive margin. We can discuss two cases: 1. If the pos/neg of the anchor simultaneously increase/decrease. this margin remains unchanged, consistent with our conclusion above. 2. If the relative difference between pos/neg in the target domain is greater/smaller than the relative difference between pos/neg in the anchor, then this margin will correspondingly decrease/increase, ultimately leading to the alignment of the distribution with the anchor.Experimentte Experimentally. we calculate the differences between the distribution of each class in the target domain and the anchor distribution before and after applying our method, using IS divergence as shown in Table 15. This further demonstrates that our online logits adjustment gradually
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Balanced Learning for Domain Adaptive Semantic Segmentation
aligns the target logits distribution with the anchor.
Table 15: The JS divergence between the Pa and corresponding anchor distribution Pp and Pn. where the results on Pa is averaged over the C negative components.
Rooi
Silowak
Buildng
Fence
Parsmn
Rider
Lrock
Train
Method
(1)
baselin
Pg
0.422
0.289
0.381
0.286
0.299
0.137
0.325
0.142
0.429
0.273
0.596
0.100
0.217
0.300
0.225
0.164
0.190
0.145
0.180
0.270
0.119
-BLDA
P2
0.367
0.246
0.361
1.153
0.312
0.221
0,146
0.234
0.281
0.175
0.310
0.344
0.00
0.273
0.254
0.181
0.125
0.207
0.125
0.217
0.072
baseline
-BI.DA
Pr
0231
0.165
0.335
0.309
0.119
0.886
0.199
0.136
0.041
K. Comparison With Other Class-inbalanced Methods
In Fig.2, we show the impact of resampling and reweighting methods on self-training. To further compare with these class-ine imbalanced methods, we adopt several common approaches, as shown in Table 16.
Table 16: Comparison with other class-imbalanced methods.
[1] (Lin et al.,
2 (Cui et al.,
3 (Hoyer et al.,
[4] (Menon al., 2020)
Ours
2017)
2019)
2022a)
Task
Object Detection
Classification
Segmentation
Classification
Segmentation
Motivation
Reweighting
Reweighting
Resampling
Logit adjustment
Logit adjustment 1. Evaluate class bias through logits sets in a form confusion matrix. 2. Ex- plicitly balance components in the matrix through anchor distributions and cumulative density functions, implemented in self-training
Implementation
Weight samples based on con- fidence, with harder samples
Define effective number of sam- ples for each class to weight differ- weights ent classes
Sample based on prior knowledge of class distribu- tion, with more sampling for rare classes
Introduce correc-
tion terms in log- its, determined by prior knowledge of class distribu- tion
receiving higher
online
paradigm.
Note
No prior knowl- edge of class dis- tribution is intro- duced. Class bias is directly mod- eled based tual logits distri- bution.
Furthermore, conduct comparative experiments in Table 17. We further discuss more class-imbalanced methods. Please refer to Appendix P for details.
L. Groups of Ovverppedictoo//nndeeepppiiiiiio
We divide the classes into two groups: over-prediction and under-prediction (refer to Fig. F(d).. Then, for our experiments, we report the performance of the two groups as shown Table 18. We observe that the main performance gains come from
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Balanced Learning for Domain Adaptive Semantic Segmentation
Table 17: Comparison with other class-imbalanced methods on GTA.+ using the mAcc (%) evaluation metric, where
baseline is based on DAFormer with uniform class sampling.
Builiing
Sidde
Pole
Ligh
Rider
Motor
Method
mAAE (T)
(1)
baseline
95.3
75.4
94.9
53.8
57.8
536
70.2
59.7
96.2
576
99.1
83.5
67.6
95.5
87.1
84.3
73.8
74.5
77.5
76.9
15.3
[2] 2017)
84.4
68.2
15.2
95.3
[3 (Hoyer et al., 2022a)
99.1
74.8
95.2
61.0
53.1
59.8
70.7
68.6
96.0
63.4
98.7
84.2
60.9
95.5
88.9
84.1
74.0
70.0
69.7
71.8
14.2
4] (Menan et ol., 2020)
97.9
79.9
95.3
64.4
64.7
0666
74.5
72.6
96.5
66.1
99.7
87.9
72.5
96.3
90.5
91.3
85.7
74.3
72.4
81.5
12.2
under-predd pedee--peeiition classes, which achieve consistent and significant improvements. For over over-prediction classes, their standard deviation (std) shows consistent decrease, indicating more balanced predictions among these classes.
Table 18: Performance comparison on GTA. grouped by over-prediction and under- under- prediction classes. The results are acquired based on CNN-based model (He et al., 2016; Chen al., 2017a), denoted C, and Transformer-base model (Xie al., 2021), denoted as T.
Metries
ToU: mean/std
Acc: mean/sid
Methods
Arch.
over- over-prediction
mnder-predictionn
over-prediction
under-prediction
DACS (Tranheden et al., 2021)
C
68.7/28.9
37.0/13.1
80.3/29.2
52.3/14.5
+BLDA
C
68.0/28. 6
42.7/14.3
79.1/29.0
59.9/13.4
DAFormer (Hoyer al., 2022a)
C
67.3/29.6
46.3/10.0
79.8/29.0
59,8/11.2
+BLDA
66.8/29.3
50.3/10.9
80.5/28.6
69.8/10.0
CDAC (Wang et al., 2023)
83.8/11.6
56.577.6
90.478.5
68.1/7.6
+BLDA
83.8/10.1 T
59.5/8.7
91.1/7.8
74.7/5.7
DAFormer (Hoyer et al., 2022a)
T
83.3/T0.3
54.777.3
90.678.0
66.1/6.2
+BLDA
T
84.2/8.5
57.8/9.4
92.3/4.7
72.6/8.0
HRDA (Hoyer el al., 2022b)
87.8/6.8
61.078.0
93.0/5.6
72.5/T0.1
+BLDA
T
88.6/6.2
64.5/7.2
93.9/4.0
77.0/8.2
MIC (Hoyer et al., 2023)
T
89.575.9
63.678.0
92.7/6.6
74.178.0
+BLDA
T
89.6/5.3
66.4/8.0
94.4/3.6
79.2/7.9
M. Visualization of Estimated GMMs
In this section, we visualize the learned GMMs for target domain, i.e., Ma for all e Fig. 7 presents the Estimated GMMs built with DAFormer, and Fig. 8 presents the estimated GMMs with introducing BLDA. We find that the estimated GMMs can accurately model logits distribution and our method reduces the difference logits distribution across classes, thus achieving balanced learning.
N. More Visualization Results of Logits Distribution
In this section, we provide more visualization results to compare logits distribution built with our method. As shown in Fig.9, for over-predicted classes, the network predicts larger positive logits and negative logits (column 1, 1) while for under-predicted classes, the network predicts smaller logits (column 2, 4). This difference in logits distribution leads to class prediction bias. Our method reduces this difference through aligning with the anchor distribution and achieves bass-balanced learning.
O. More Qualitative Results
In this section, we provide more qualitative results between our method and other competitors on GTA-CS. As shown in Fig. 10, when previous methods fail to recognize the classes that are under-predicted and suffer severe performance decline in UDA (c.g. sidewalk, pole,fence the effectiveness of our method.
fence, terrain, bige,,,gn), BLDA shows significant improvement on them, thereby demonstrating
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Balanced Learning for Domain Adaptive Semantic Segmentation
Figure 7: Estimated GMMs on target domain built with DAFormer.
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Balanced Learning for Domain Adaptive Semantic Segmentation
Figure 8: Estimated GMMs on target domain built with BLDA.
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Balanced Learning for Domain Adaptive Semantic Segmentation
Traffic Light
Train
Leght Daer burton
Cirt Oan buldin
Lagit DIsrBLLSR
Building
EL BELLEONN
Vegetation
705
702
14T1 DAVIUEE OHS LIghT
Lignt DICDAASY Clui Ca
LaINs DIREA Claa
5150
Car
LEBOSERA Ous Sky
Sky
Traffic Sign
Bicyde
01-
LOg Darbbuse ClUY Royoe
423
Car
3.00
500
Logn Duttuterrrrcoo
BICEK
Sky
312
330
Figure 9: Comparison of Logits Distribution. We choose {building (2), vegetation (8), car (13) sky (10) as over-predicted classes, and (train (16), bicycle (18), traffic light (6), traffic sign (7) } as under-predicted classes for visualization. Note that the anchor distribution is counted separately at baseline and our method.
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Balanced Learning for Domain Adaptive Semantic Segmentation
Image
DAFormer
DAFormer+Bi +BLDA
MIC
MIC+BLDA
Ground Truth
Figure 10: More qualitative comparison with DAFormer and MIC. The yellow boxes mark regions improved by BLDA.
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Balanced Learning for Domain Adaptive Semantic Segmentation
P. More Discussion with Related Work
Since semantic segmentation involves assigning a label to every pixel in an image, it often incurs high annotation costs (Chen et al., 2024; Li et al., 2023; Wang et al., 2024). To address this, various label-efficient approaches have been proposed. including semi-supervised learning (Sun et al., 2025; Mai al., 2024b), few-shot learning (Li et al., 2025; Luo et al., 2024). and domain adaptation (Li et al., 2024; Chen et al., 2025). In this work, primarily focus on the setting of unsupervised domain adaptation.
P.1. Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation (UDA) transfers semantic knowledge learned from labeled source domains to unlabeled target domains. Due to the ubiquity of domain gaps (Wangkai et al., 2023; Pan et al., 2024), UDA methods have been widely studied in various computer vision tasks, such as image classification, object detection, and semantic segmentation. UDA is crucial for semantic segmentation to avoid laborious pixel-wise annotation in new target scenarios.
Recent UDA approaches for semantic segmentation fall into two main paradigms: adversarial training- based methods (Toldo et al.. 2020; Tsai et al., 2018; Chen al., 2018; Ganin & Lempitsky, 2015; Hong et al., 2018; Long al., 2015b) and self-training- methods (Tranheden et al., 2021; Hoyer et al., 2022a; Araslanov & Roth, 2021; Zhang et al., 2021). Adversarial training-basse based methods learn domain- invarrint representations through a min-max optimization game, where feature extractor is trained to confuse a domain discriminator, aligning feature distributions across domains. Self-training- based methods, which have become dominant in the field due to the domain robustess of Transformers (Bhojanapalli et al., 2021), generate pseudo labels for target images based on a teacher-student optimization framework. The success of this paradigm depends on generating high-quality pseudo labels, with strategies such as entropy minimization (Chen et al., 2019) and consistency regularization (Hoyer et al., 2023) being developed for this purpose.
Recently, several approaches have been proposed to tackle the challenges in UDA for semantic segmentation from different perspective: DTS (Huo et al., 2023) employs a Dual Teacher-Studen Framework, promoting the self-training paradigm to fully adapt models to the target domain by exploring different mix strategies. CDAC (Wang et al., 2023) introduces consistency constraints in attention to enhance the model's 's cross-domain performance. RTea (Zhao el al., 2023) defines proxy tasks based on structural information and incorporates them into the self-training paradigm as additional supervision signals. Peng et al. (2023) applics Diffusion- based image translation techniques to directly mitigate the distribution differences between the target and source domains. DiGA (Shen et al., 2023) integrates distillation strategies and self traininr through multi-stage training. Copt (Mata al., 2025) uses domain-agnostic text embeddings to learn domain-invariant features in image segmentation encoder. MICDrop (Yang et al., 2025) learns joint feature representation by masking image encoder features while inversely masking depth encoder features to leverage geometric information. ADFormer (He & Todorovic, 2025) introduces a new transformer by decomposing cross-attention in the decoder into domain- independent and domain-specific parts
However, due to the inherent class imbalance and distribution shift in both data and label space between domains, networks often exhibit complex class biases, which are further amplified by the confirmation bias inherent in the self self-training paradigm. Our method aims achieve balanced learning in UDA training mitigate these issues and improve the overall performance of domain adaptation for semantic segmentation.
P.2. Class-Imbalanced Learning
Class imbalance is a prevalent issue in semantic segmentation, where the number of samples per class varies significantly. Existing methods tackle this problem through re- re-weighting or re-sampling techniques. Re-weighting methods assign different weights to classes during training, giving higher importance to under-represented classes (Cui et al., 2019; Lin et al., 2017; Cao et al., 2019; Liu et al., 2019). Re-sampling techniques modify the class distribution in the training data by over-sampling minority classes or under-sampling majority classes (He et al., 2008; 2021; He & Garcia, 2009; Kim et al., 2020; Chu et al., 2020).
Recent works have proposed various approaches address class imbalance in different tasks. For object detection, Lin al. (2017) propose re-weighting method that assigns weights to samples based their confidence, with harder samples receiving higher weights. In classification,, Cui et al. (2019) define an effective number of samples for each class to weight different classes, while Menon et al. (2020) introduce a logit adjustment method that incorporates correction terms in logits, determined by prior knowledge of class distribution. For segmentation, Chu et al. (2021) propose a stochastic training
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Balanced Learning for Domain Adaptive Semantic Segmentation
scheme for semantic segmentation, which improves the learning of debiased and disentangled representations, and Wang al. (2021a) propose Seesaw loss that reweights the contributions of gradients produced by positive and negative instances of class for instance segmentation. Sun et al. (2025) introduces generative approach that alleviates bias by modeling the task as conditional discrete data generation with a mathematically derived debiasing strategy.
In UDA for semantic segmentation, several approaches have introduced these strategies to alleviate class bias: DAFormer (Hoyer et al., 2022a) present a re-sampling technique that samples based on prior knowledge of class distribution, with more sampling for rare classes. Freedom (Truong al., 2023) considers haass-imbllance as faimess problem and propose to model the context of structural dependency to tackle it. SAC (Araslanov & Roth, 2021) cit maintains an exponentially moving class prior used to discount the confidence thresholds. CPSL (Li et al., 2022) employs distribution alignment technique to enforce the marginal class distribution of cluster assignments to be close to that of pseudo labels. However, these methods are still empirical and focus on the single-domain setting, which assumes that the test data and training data share the same distribution in both data space and label space, without considering the additional challenges posed by domain shift in UDA.
In contrast to these methods, our approach aims to directly address class bias and achieve balanced learning for each class without relying on prior knowledge about the distribution shift between domains. We evaluate class bias through logit sets in the form of confusion matrix and explicitly balance components in the matrix using anchor distributions and cumulative density functions, implemented in an online self-training paradigm. Our method does not depend on prior knowledge of class distribution and instead directly models class bias based on the actual logit distribution, making it more adaptable to the challenges posed by domain shift in UDA.
P.3. Further Comparison with Existing Methods
While our method shares some similarities with existing works (Zhang et al., 2019; Ning et al., 2021; Wang et al., 2021b; Moradinasab et al., 2024) in terms of introducing anchor distributions for alignment and using GMMs for distribution estimation, our approach fundamentally differs in its goal of addressing the unique challenge of class bias in UDA settings. Specifically:
Previous methods focus primarily on mitigating domain discrepancy through feature alignment strategies. For example, Zhang et al. (Zhang et al., 2019) use class- wise anchors to represent source domain distributions and align target domain features accordingly. Ning et al. (Ning et al., 2021) employ multiple anchors to model potentially multimodal category distributions and select target samples with high discrepancy for active learning. Wang et al. (Wang et al., 2021b) leverage contrastive learning on target domain features using prototypes modeled by GMMs on the source domain. Moradinasab et al. (Moradinasab et al., 2024) identifl reliable pscudo-labels through uncertainty modeling with GMMs.
In contrast, our method proposes a class balanced learning strategy targeting class bias, rather than the feature alignment strategies employed by previous methods to address domain differences. We propose complete methodology (why-what- how) specifically for UDA settings. We first analyze the unique class bias challenge, distinct from regular class-imbalances problems (why). Then, based on this unique challenge, we carefully design a class balanced learning strategy to estimate correction terms from a logits distribution perspective (whar). Finally, to implement this strategy, we first implement distribution estimation as a prerequisite, then prepend to delicate online logits adjustment to perceive the model's learning state, towards class-balanced learning (how).
Q. Limitation
Our method analyzes the class bias in domain adaptive semantic segmentation through logits distribution statistics and propose method to implement online logits adjustment tailored for the UDA training process, which can be easily built with exiting methods and demonstrate consistent improvements. Although BLDA achieves remarkable performance, we balance the class under the assumption that each logits distribution in M is independent, without considering correlation between classes. How to model this correlation and mitigate the class bias further is still to be resolved.
26


Reviews:
[{"review_id": "zzxfsh36pZ", "reviewer": "Reviewer_ZPAk", "summary": "The study conducts an in-depth investigation into class bias within UDA scenarios, demonstrating that this bias stems from simultaneous shifts in both the label and data distributions, which complicates the domain adaptation process. To address this challenge, the authors introduce a novel approach that evaluates and reduces class bias through a class-balanced learning method. This approach derives adjustment factors from the distribution of logits, thereby overcoming the constraints inherent in conventional imbalanced class techniques. Notably, the proposed solution is implemented as a versatile plug-and-play module, suitable for broad applications in UDA. It begins by estimating distribution parameters, then applies dynamic logit adjustments to monitor the model’s learning progression, ensuring balanced class performance. Extensive experiments confirm that the method consistently yields significant improvements in performance, highlighting its effectiveness and flexibility.\n\n## update after rebuttal\nThanks for the authors' detailed response. The response has addressed most of my concerns. I hope the authors can revise their paper according to the reviewers' suggestions. I will keep my original rating.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the positive and constructive feedback, as well as for acknowledging our contributions,  including the **clear problem formulation**, the **clarity of the writing**, and the **effectiveness of  empirical validation**. We address each of your concerns point by point.\n\n---\n\n**Q1: Computation Overhead** \n\n**A1:** We provide a detailed analysis of the computational overhead in Appendix I, including actual resource usage and training time across various baselines. In summary, the additional cost introduced by our proposed components stems from three main operations. We have implemented them efficiently to minimize overhead:\n\n1. **GMM Implementation**: Instead of using off-the-shelf libraries that update Gaussian parameters sequentially, we store the parameters of all $C \\times C \\times K$ components as tensors in PyTorch and update them in parallel using matrix operations.\n2. **CDF Computation**: We approximate the cumulative distribution function using the Abramowitz-Stegun formula, which allows efficient polynomial evaluation.\n3. **Inverse CDF Computation**: We use interpolation techniques within the estimated value range, which avoids costly numerical inversion.\n\nAll the above operations can be efficiently performed using simple matrix operations on tensors. Moreover, the storage of Gaussian component parameters and the additional regression head (a $1\\times 1$ conv) introduced by our method are lightweight. Overall, our method demonstrates high efficiency in both training time and GPU memory, as reported in Table 13 of Appendix I. Moreover, our method introduces no additional overhead during inference.\n\n---\n\n\n**Q2: Why use logits distributions**\n\n**A2:**  In UDA, explicit class distribution priors are often unavailable, especially in the target domain, where distribution shift is severe. Therefore, we propose leveraging the logits distributions as an online proxy to assess a model’s class-wise prediction bias.\n\nWe justify this design choice from both theoretical and empirical perspectives:\n\n1. Theoretical Justification:\n    As shown in Definition 2, the prediction bias $\\mathrm{Bias}(l)$ is directly related to the probability of predicting class $l$ across all ground-truth classes, which in turn depends on the relative distributions of logits. We further show in Eq. (5) that under the assumption of independent logits distribution, this probability can be estimated by comparing the positive and negative class logits. A sufficient condition for unbiased prediction is that these distributions are aligned.\n\n2. Empirical Evidence:\n    In Figure 1(d), we demonstrate a clear linear correlation between the prediction bias and the differences in logits distributions across classes. This supports our hypothesis that logits distributions serve as an effective proxy to capture class imbalance in the network’s behavior.\n\n   \n---\n\n\n**Q3: Alternative Distribution Estimation Methods**   \n\n**A3:** Thank you for this valuable suggestion. We chose  GMMs primarily for their balance between modeling capacity and computational efficiency, which is critical for our online training setup. Specifically:\n\n- GMMs can approximate a wide range of 1D distributions with a small number of parameters.\n- They allow closed-form CDF and inverse CDF computation (using polynomial approximations), enabling efficient matrix-based implementation for large-scale training.\n- The distributions we model are scalar-valued logits for each class pair $(c, l)$, making GMMs a sufficiently expressive and computationally tractable choice.\n\nAdditionally, as shown in Appendix M, GMMs empirically fit the logits distributions well, and as discussed in Appendix E, they converge quickly during training. While more complex estimators (e.g., kernel density estimation or deep density models) could be considered, they often introduce significant overhead and do not offer closed-form CDFs, which are essential for our framework.\n\n\n---\n\n**Q4: References**\n\n**A4:** Thank you for pointing this out. These references are indeed important and representative works in UDA for semantic segmentation. We will include them in the Related Work section in the revised version and  compare with them in our experiments.\n\n---\n\nWe hope our response can resolve your concern. Please do not hesitate to let us know if you have further questions.", "claims_and_evidence": "The experimental claims are supported with thorough empirical results. However, my concern is the authors need further justification about why using logits distributions as a proxy for class imbalance. Additionally, the method needs comparison against alternative distribution estimation methods except using Gaussian Mixture Models.", "methods_and_evaluation_criteria": "The selected metrics—mIoU and mAcc are well-suited for assessing performance, as they capture both the precision of segment overlap and overall classification accuracy. Additionally, the method itself is thoughtfully designed, with a clear and sound intuition underpinning its architecture and approach.", "theoretical_claims": "The theoretical claims about the connection between logits distribution and class biases are well-defined. The authors clearly depict their relationships and provide convincing theoretical justifications.", "experimental_designs_or_analyses": "The experimental systematically demonstrating improvements introduced by BLDA. The authors conduct extensive ablations on each model and various configurations, which strongly support the design.", "relation_to_broader_scientific_literature": "The work is well related with domain-adaptive semantic segmentation literature, clearly point out the limitations of prior class-balancing methods such as re-weighting and re-sampling.", "essential_references_not_discussed": "There are plenty of domain adaptive semantic segmentation methods. I know it is not possible for the authors to plug their method to each of them to show the effectiveness of their method, but some typical methods representing different technical solutions need to be compared. For example, comparing to the works listed below\n\n[1] Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018.\n\n[2] Confidence regularized self-training, ICCV 2019. \n\n[3] Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation, NeurIPS 2020.", "other_strengths_and_weaknesses": "Strengths:\n\n-Clear written paper.\n\n-Clearly identified problem of class imbalance unique to UDA.\n\nWeaknesses:\n\n-Lack of analysis concerning computational overhead and scalability.\n\n-Need further justification about why using logits distributions as a proxy for class imbalance.\n\n-Need further comparison against alternative distribution estimation methods except using Gaussian Mixture Models.", "other_comments_or_suggestions": "No other comments.", "questions_for_authors": "Please refer to Other Strengths And Weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Y5qiiwzkJ0", "reviewer": "Reviewer_j9eK", "summary": "The paper proposes Balanced Learning Domain Adaptation for addressing class bias in unsupervised domain adaptative semantic segmentation. The authors identify class imbalance and distribution shifts as major obstacles in UDA and propose techniques to analyze logit distributions to assess prediction bias. The method introduces post-hoc logits adjustment and online logits adjustment to mitigate class bias and improve balanced learning across classes. Additionally, cumulative distribution estimation is used as domain-shared structural knowledge. The paper demonstrates significant performance improvements on standard UDA benchmarks.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the positive and constructive feedback. We appreciate your recognition of **our method’s versatile   design**, the **thoroughness of our experimental validation**, and the **clarity of our theoretical explanation**. We address each of your concerns point by point.\n\n---\n\n\n**Q1: Computational Cost and Scalability of GMM-based Modeling** \n\n**A1:**  We provide a detailed analysis of the computational overhead in Appendix I, including actual resource usage and training time across various baselines. In summary, the additional cost introduced by our proposed components stems from three main operations. We have implemented them efficiently to minimize overhead:\n\n1. **GMM Implementation**: Instead of using off-the-shelf libraries that update Gaussian parameters sequentially, we store the parameters of all $C \\times C \\times K$ components as tensors in PyTorch and update them in parallel using matrix operations.\n2. **CDF Computation**: We approximate the cumulative distribution function using the Abramowitz-Stegun formula, which allows efficient polynomial evaluation.\n3. **Inverse CDF Computation**: We use interpolation techniques within the estimated value range, which avoids costly numerical inversion.\n\nAll the above operations can be efficiently performed using simple matrix operations on tensors. Moreover, the storage of Gaussian component parameters and the additional regression head (a $1\\times 1$ conv) introduced by our method are lightweight. Overall, our method demonstrates high efficiency in both training time and GPU memory, as reported in Table 13 of Appendix I.  Moreover, our method introduces no additional overhead during inference.\n\nFor larger datasets, i.e., with more classes, the only change is that the GMM module needs to store a proportionally larger number of Gaussian components, which remains tractable in practice.\n\n---\n\n**Q2: Generalization of Pre-defined Anchor Distributions**   \n\n**A2:** We provide a detailed discussion of the anchor distribution design and its alternatives in Appendix J. A summary is provided below:\n\n1. **Definition and Role of Anchor Distributions:**\n    We use the global positive and negative logits distributions from the source domain to estimate a shared anchor distribution for both the source and target domains. This shared anchor allows the target logits distribution to gradually align with the source, serving two purposes:\n\n   - It provides a reference to balance learning progress across classes within each domain.\n   - It acts as a bridge to align the source and target domains in terms of class-wise logits behavior.\n\n2. **Different Selection Criteria for Anchor Distributions:**\nWhile the anchor distribution is estimated from the source domain, we acknowledge that some discrepancy may exist between this estimation and the true distribution of the target domain. However, our analysis in Appendix B shows that the relative pos/neg bias is more important than the absolute value of the logits, and such relative structure tends to be preserved across domains due to the observation that the biases of positive and negative logits are coupled in Fig.1 (c). This explains why the impact of distributional mismatch is limited in practice.\n\n      We also conduct ablation studies (Table 14) on different anchor selection strategies, including: Global source distribution (default); Global target distribution (oracle); Two biased classes (*building* and *fence* in Fig. 1(d)). The results show that different anchor choices lead to only marginal differences in performance, further demonstrating the robustness of our method to anchor selection and its ability to generalize across domain shifts.\n\n3. **Generalization Across Datasets and Domain Shifts:**\n    Our logits adjustment mechanism can be interpreted as a cross-entropy loss with an adaptive margin. As discussed in Appendix B, this margin is implicitly determined by the relative difference between the positive and negative logits distributions. There are two key cases:\n   - If both positive and negative logits in the anchor increase or decrease simultaneously, the margin remains stable.\n   - If the relative gap between pos/neg distributions in the target differs from that in the anchor, the margin adapts accordingly to guide alignment.\n\n   This behavior enables the method to generalize across domain shifts. As demonstrated in Table 15,   the adaptive margin mechanism drives the target logits distribution to progressively align with the anchor distribution over training, confirming the intended behavior of our method.\n\n---\n\nWe hope our response can resolve your concern. Please do not hesitate to let us know if you have further questions.", "claims_and_evidence": "1. The claim that logit distribution differences correlate with class bias is well-supported by experimental visualizations of FIG 6.\n2. The effectiveness of online logits adjustment and post-hoc adjustment is supported by ablation studies。\n3. The effectiveness of Balanced Learning is supported by consistent improvements in mIoU and mAcc metrics across several baselines and benchmarks.", "methods_and_evaluation_criteria": "1. The proposed methods, including post-hoc logits adjustment and online logits adjustment, are well-suited to address the stated problem of class bias in UDA.\n2. The use of standard UDA benchmarks ensures fair and meaningful evaluation.", "theoretical_claims": "No major theoretical claims were presented beyond the statistical modeling of logits distributions and their alignment.", "experimental_designs_or_analyses": "The experimental designs are sound, with appropriate baselines and thorough ablation studies.\nThe use of mIoU and mAcc metrics is effective for assessing both overall accuracy and balanced performance across classes.", "relation_to_broader_scientific_literature": "The idea of using logits distribution analysis is related to class-imbalanced learning and focusing on distribution shifts between domains.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "Strengths:\n1. The method is versatile and can be integrated with various self-training-based UDA frameworks.\n2. The authors provide comprehensive experiments, including multiple benchmarks and ablation studies.\n3. The paper provides a clear theoretical explanation for logit distribution alignment and its connection to class bias, grounding the proposed method in established statistical principles.\n\nWeakness:\n1. The GMM-based logits modeling may be computationally expensive, especially for larger datasets.\n2. The reliance on pre-defined anchor distributions may not generalize well to all datasets or domain shifts. The paper lacks discussion on alternative approaches, such as learned or adaptive anchors.", "other_comments_or_suggestions": "1. The scalability of the method should be discussed, especially in terms of computational overhead introduced by GMMs.", "questions_for_authors": "1. How does the method scale to larger datasets or real-time applications, given the computational cost of GMM-based logits modeling?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qnnYHiBCvw", "reviewer": "Reviewer_41Vm", "summary": "This paper presents Balanced Learning for Domain Adaptation (BLDA), an innovative approach to address class imbalance and distribution shifts in Unsupervised Domain Adaptation (UDA) for semantic segmentation. Specifically, it identifies over-predicted and under-predicted classes through the analysis of predicted logits and employs a post-hoc approach to align logits distributions across different classes using shared anchor distributions. During self-training, BLDA estimates logits distributions online and incorporates correction terms into the loss function to ensure unbiased pseudo-label generation. Extensive experiments on standard UDA benchmarks have demonstrated that BLDA consistently improves performance, particularly for under-predicted classes, when integrated with various existing methods.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewers for their valuable feedback and thoughtful comments. We appreciate the recognition of our **clear writing**, **comprehensive experiments**, and **extensive qualitative analyses**. We address each of your concerns point by point.\n\n---\n\n**Q1: Clarification on Eq. (4) and the Definition of Positive Bias**\n\n**A1:**  Sorry for any misunderstanding. In Eq. (4), the expression $\\arg\\max_{c' \\in [C]} f_\\theta(x)[c']$ refers to the predicted class for input $x$.  $P(\\arg\\max_{c' \\in [C]} f_\\theta(x)[c'] = l \\mid y = c)$  represents the probability that a sample from class $c$ is predicted as class $l$. There is no constraint that $c \\ne l$ in Eq. (4). The summation in this equation is taken over the conditioning variable $c$, i.e., it averages the conditional probabilities $P(\\arg\\max_{c' \\in [C]} f_\\theta(x)[c'] = l \\mid y = c)$ across all classes $c \\in [C]$.\n\nBy summing over all $c$ and taking the average, we obtain the expected probability that a sample from any class (including $l$ itself) is predicted as class $l$. Under an unbiased model, this expectation should be approximately $1/C$. Therefore, a positive bias indicates that class $l$ is over-predicted, as its average prediction probability exceeds the uniform expectation.\n\n---\n\n**Q2: Dependence on Baseline Models**\n\n**A2:** Our method is designed as a plug-and-play module that can be integrated into any self-training-based UDA framework. Naturally, its performance is influenced by the underlying baseline, especially when estimating the target-domain logits distribution, which relies on the pseudo-labels generated during self-training. Hence, the quality of pseudo-labels affects the accuracy of our distribution estimation and, consequently, the effectiveness of the class-aware adjustment.\n\nHowever, we emphasize that BLDA consistently reduces class-level prediction variance across all baselines. As reported in the main paper, the standard deviation of per-class IoU and Acc drops significantly when BLDA is applied. This demonstrates that our method consistently yields more balanced predictions with reduced class bias, regardless of the baseline.\n\n---\n\n**Q3: Fairness of Comparisons and Pseudo-Label Quality**\n\n**A3:** We would like to clarify that our method adheres to the standard UDA setting and does not modify the pseudo-label generation process of any baseline. As described in Section 3.2, we employ the self-training framework as implemented in existing works such as DACS, DAFormer, CDAC, HRDA, and MIC. All these methods adopt an online self-training protocol: the model is trained from scratch using labeled source data and unlabeled target data with pseudo-labels generated during training.\n\nBLDA is inserted into this process as a lightweight module that adjusts the logits distributions based on estimated class-wise prediction behavior. The pseudo-label generation and training pipeline of each baseline remains unchanged.\n Therefore, the comparisons are conducted under fair and consistent settings,  where each method follows the same online self-training paradigm (note that all baselines train the model from scratch, without using a source-only pretrained model).\n\n\n---\n\n**Q4: Computation Overhead** \n\n**A4:** We provide a detailed analysis of the computational overhead in Appendix I, including actual resource usage and training time across various baselines. In summary, the additional cost introduced by our proposed components stems from three main operations. We have implemented them efficiently to minimize overhead:\n\n1. **GMM Implementation**: Instead of using off-the-shelf libraries that update Gaussian parameters sequentially, we store the parameters of all $C \\times C \\times K$ components as tensors in PyTorch and update them in parallel using matrix operations.\n2. **CDF Computation**: We approximate the cumulative distribution function using the Abramowitz-Stegun formula, which allows efficient polynomial evaluation.\n3. **Inverse CDF Computation**: We use interpolation techniques within the estimated value range, which avoids costly numerical inversion.\n\nAll the above operations can be efficiently performed using simple matrix operations on tensors. Moreover, the storage of Gaussian component parameters and the additional regression head (a $1\\times 1$ conv) introduced by our method are lightweight. Overall, our method demonstrates high efficiency in both training time and GPU memory, as reported in Table 13 of Appendix I.  Moreover, our method introduces no additional overhead during inference.\n\n---\n\nWe hope our response can resolve your concern. Please do not hesitate to let us know if you have further questions.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "There is some confusion about Eq.(4).  As $\\mathbb{P}(\\arg max_{c' \\in [C]}f_{\\theta}(x)[c']=l\\|y=c)$ represents the probability of predicting class $c$ as $l$ and $c \\neq l$, why does the positive bias $\\mathrm{Bias}(l)$ indicates over-prediction? Given the condition $y=c$, how do we explain the representation of the summation of $c' \\in [C]$? As my understanding, it may be $\\mathbb{P}(\\arg max_{l \\in [C]}f_{\\theta}(x)=l\\|y=c)$.", "experimental_designs_or_analyses": "This paper has constructed extensive experiments on the standard UDA benchmarks to demonstrate the effectiveness of the proposed post-hoc method.", "relation_to_broader_scientific_literature": "The class imbalance problem is widely studied in semantic segmentation, cross-domain semantic segmentation, and other perception fields. This paper proposes a post-hoc method to balance over-prediction and under-prediction classes during domain adaptation, which is straightforward and makes sense.  Moreover, the proposed method may cost a lot of time and computational resources due to the multiple GMMs.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths: \n\n1. The paper is generally well-written, well-structured, and easy to follow.\n2. The experiments are comprehensive, covering three transfer tasks for segmentation, an additional image classification task (included in the supplementary materials), and extensive qualitative analyses.\n\nWeaknesses: \n1. The paper claims to stress the class-imbalanced problem by the proposed BLDA. However, it seems like the proposed method profoundly depends on the baseline models. For example, the performance of \"Train\" in Table 1 is close to 0 for DACS and DAFormer (C), which shows that the proposed method may still suffer from the class-imblanced problem.\n2.  Concerns regarding the fairness of the comparisons. In Tables 1 and 2, the experiments seem to leverage high-quality pseudo-labels for self-training, which may inherently provide an advantage over existing methods, such as DAFormer, CDAC, HRDA, and MIC. This raises questions about whether the improved performance is due to the proposed method's unique contributions or simply a result of the enhanced quality of the pseudo-labels used.\n3. It would be more appropriate to compare the performance with the existing method beginning with the same source-only model.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "The major question is about Eq. 4. As $\\mathbb{P}(\\arg max_{c' \\in [C]}f_{\\theta}(x)[c']=l\\|y=c)$ represents the probability of predicting class $c$ as $l$ and $c \\neq l$, why does the positive bias $\\mathrm{Bias}(l)$ indicates over-prediction? Given the condition $y=c$, how do we explain the representation of the summation of $c' \\in [C]$? As my understanding, it may be $\\mathbb{P}(\\arg max_{l \\in [C]}f_{\\theta}(x)=l\\|y=c)$.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CNElO1xQIV", "reviewer": "Reviewer_egQB", "summary": "The paper proposes Balanced Learning for Domain Adaptation (BLDA) to address class bias in unsupervised domain adaptation (UDA) for semantic segmentation. BLDA analyzes logits distributions to assess prediction bias and introduces an online logits adjustment mechanism to balance the class learning in both source and target domains. Experimental results demonstrate consistent performance improvements when integrating BLDA with various methods on standard UDA benchmarks.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the positive and constructive feedback. We appreciate your recognition of  **motivation**, **theoretical intuition**, and  **clear writing**, as well as your kind comments on our visualizations and formula design.\n\n---\n\nRegarding your suggestion to include more experimental results for the Cityscapes → ACDC setting, we agree that evaluating BLDA under more diverse and challenging UDA scenarios is important and we conduct extended experiments on image classification/video semantic segmentation in Appendix G/H. We conduct additional experiments on the Cityscapes → ACDC benchmark and present the results below.\n\nAs shown in the following tables (∗ denotes the reproduced result), integrating BLDA consistently improves performance across multiple baselines, including DACS, DAFormer, and MIC, on both mIoU and mAcc metrics. Importantly, BLDA is designed as a plug-and-play module that is model-agnostic and can be seamlessly integrated into most existing UDA baselines without modifying their core architectures or training objectives. Since class bias is a pervasive issue across UDA settings, our method provides a general mechanism to mitigate this imbalance during training. As such, BLDA is able to consistently improve performance across a wide range of models and target domains, as demonstrated in our experiments on both standard benchmarks and the newly added Cityscapes → ACDC setting.\n\n**Cityscapes → ACDC (IoU, %)**\n\n| Method| Arch. |Road|Sidewalk|Building|Wall|Fence|Pole|Light | Sign  | Veg | Terrain  | Sky  | Person | Rider    | Car  | Truck    | Bus      | Train    | Motor    | Bike     | **mIoU** | **std**  |\n|-|-|-|-|-|-|-|-|-|-|- |- |- |-|- |- |- | - | - |- | - | - |- |\n| DACS* | C | 78.5 | 36.3  | 73.4 | 28.7 | 17.0  | 42.2 | 57.1  | 45.6  | 68.3 | 26.7 | 75.0| 51.3 | 24.5  | 75.6 | 37.8 | 43.1 | 41.3 | 28.8     | 26.8     | 46.0     | 19.3     |\n| **+BLDA** | C  | 74.6 | **41.9** | 67.3     | **31.5** | **19.8** | **46.9** | 54.8  | **50.9** | **71.7** | **29.5** | 69.4     | **54.6** | **26.6** | 74.2 | **41.9** | **45.0** | **49.8** | **31.1** | **27.9** | **47.9** | **17.1** |\n| DAFormer* | T   | 65.5 | 52.8     | 79.1     | 39.8     | 37.2 | 56.4 | 57.6  | 51.3  | 72.2| 37.3| 59.9     | 54.7     | 25.0     | 83.6 | 69.6     | 68.0     | 72.9     | 39.6     | 33.2     | 55.5     | 16.3     |\n| **+BLDA** | T  | 63.8 | 50.7     | 73.4     | **40.2** | **40.7** | 53.3     | 54.6  | 50.0     | 70.7     | **40.3** | **64.3** | **58.9** | **33.9** | 83.5 | **74.3** | **75.5** | **78.9** | **45.1** | **37.6** | **57.4** | **15.2** |\n| MIC*  | T     | 89.5 | 63.0 | 86.3 | 56.7 | 46.2     | 64.2     | 65.8  | 64.2     | 75.5     | 46.9     | 83.2     | 67.9     | 45.6     | 87.7 | 85.9     | 92.3   | 88.7| 54.0| 55.4   | 69.4 | 15.9 |\n| **+BLDA** | T     | 88.7 | **66.8** | **87.8** | **61.1** | **48.9** | 63.3     | 65.2  | **67.3** | 73.7     | **50.5** | 82.4     | **68.2** | **49.5** | 86.7 | 80.5  | 91.1 | **91.0** | **54.6** | **61.8** | **70.5** | **14.2** |\n\n**Cityscapes → ACDC (Acc, %)**\n\n| Method    | Arch. | Road  | Sidewalk | Building | Wall     | Fence    | Pole     | Light    | Sign  | Veg  | Terrain  | Sky | Person   | Rider    | Car      | Truck    | Bus      | Train    | Motor    | Bike     | **mAcc** | **std**  |\n| -- | - | -| --| - | - | -| -- | - | - | - | -- | --- | --- | --- | --- | -- | -- | ---- |--- | ---- | --- | - |\n| DACS*     | C     | 96.7     | 52.9     | 81.9     | 39.2     | 36.3     | 51.8     | 73.1     | 55.0     | 90.7 | 35.0     | 76.1     | 60.1     | 33.3     | 84.5     | 44.3     | 44.2     | 59.7     | 35.8     | 39.8     | 57.4     | 20.0     |\n| **+BLDA** | C     | 96.1     | **66.7** | **82.7** | **40.7** | **36.6** | **58.9** | 66.4     | **67.4** | 84.1 | **41.3** | 70.7     | **68.4** | **36.3** | **84.8** | **48.9** | **46.5** | **59.9** | **47.4** | **41.4** | **60.3** | **17.7** |\n| DAFormer* | T  | 98.2| 63.4  | 86.7  | 51.2| 42.0  | 70.3     | 77.3     | 65.1     | 93.4 | 52.6     | 60.6     | 60.3     | 53.7     | 90.7     | 89.3 | 70.8  | 90.3 | 57.8| 42.7 | 69.3  | 17.4 |\n| **+BLDA** | T | **98.8** | 61.3     | 85.5   | **55.8** | **45.4** | **73.2** | **82.5** | **72.8** | 92.0 | 51.7     | **65.0** | **69.2** | 51.5 | 90.5| **89.5** | **80.0** | **92.8** | 60.6 | **51.1** | **72.1** | **16.4** |\n| MIC* | T  | 99.4  | 69.2  | 92.2 | 76.0 | 57.7| 73.5 | 89.5     | 80.9     | 94.9 | 62.8  | 89.3 | 82.7     | 57.5     | 97.6     | 91.9  | 96.9     | 96.6     | 60.2     | 72.6     | 81.1     | 14.2     |\n| **+BLDA** | T  | 98.8     | **72.3** | **93.8** | **86.1** | **59.7** | **73.6** | **90.8** | **89.9** | 93.2 | **73.9** | **90.8** | 81.4     | **68.3** | 96.3 | **94.8** | **97.0** | **97.6** | **65.3** | 74.1 | **84.1** | **12.1** |\n\nWe will include these results in the revised version of the paper.\n\n---\n\nWe hope our response can resolve your concern. Please do not hesitate to let us know if you have further questions.", "claims_and_evidence": "The claims made in the submission are supported by clear and convincing evidence.", "methods_and_evaluation_criteria": "The proposed method makes sense for the problem and experiment results demonstrate its effectiveness.", "theoretical_claims": "I have checked the correctness of any proofs for theoretical claims. The paper states that \"the distribution of logits predicted by the network can assess the degree of class bias\", which is demonstrated by Fig.1(c) and Fig.1(d).", "experimental_designs_or_analyses": "I have checked the experimental results. What are the experimental results of unsupervised domain adaptation at Cityscapes to ACDC[1]？\n[1] ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding. ICCV 2021", "relation_to_broader_scientific_literature": "BLDA analyzes logits distributions to assess prediction bias and introduces an online logits adjustment mechanism to balance class learning in both source and target domains. It inspires more researchers to utilize logit distributions to assess prediction bias.", "essential_references_not_discussed": "No related works that are essential to understanding the context for key contributions of the paper, but are not currently cited/discussed in the paper.", "other_strengths_and_weaknesses": "The writing is excellent, the charts are beautiful, and the formulas are exciting!\nThe experiments are slightly lacking and would like to show more experimental results for unsupervised domain adaptation settings such as Cityscapes to ACDC.", "other_comments_or_suggestions": "No", "questions_for_authors": "What are the experimental results of unsupervised domain adaptation at Cityscapes to ACDC[1]？\n[1] ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding. ICCV 2021", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: All reviewers acknowledged that the paper presents solid novelty and is well-written, well-structured, and easy to follow. Initially, some concerns were raised regarding the limited scope of experiments, particularly the lack of comparisons with alternative distribution estimation methods and the fairness of existing comparisons. However, during the rebuttal phase, the authors addressed these concerns by providing additional experiments. AC has also reviewed the paper, the reviewer comments, and the rebuttal, and agrees that the motivation is clear, the writing is strong, and the experimental evaluation is thorough. Therefore, the AC recommends acceptance. It is recommended that all additional experiments and discussions from the rebuttal be incorporated into the final version of the paper.
Decision: Accept (poster)