Beyond Confidence: Exploiting Homogeneous Pattern for Semi-Supervised Semantic Segmentation
Authors: Rui Sun
Huayu Mai
Wangkai Li
Yujia Chen
Naisong Luo
Yuan Wang
Tianzhu Zhang
Venue: ICML 2025 poster (2025)
Keywords: Semantic Segmentation
Primary area: applications->computer_vision

Abstract: The critical challenge of semi-supervised semantic segmentation lies in how to fully exploit a large volume of unlabeled data to improve the model's generalization performance for robust segmentation. Existing methods mainly rely on confidence-based scoring functions in the prediction space to filter pseudo labels, which suffer from the inherent trade-off between true and false positive rates. In this paper, we carefully design an agent construction strategy to build clean sets of correct (positive) and incorrect (negative) pseudo labels, and propose the Agent Score function (AgScore)  to measure the consensus between candidate pixels and these sets. In this way, AgScore takes a step further to capture homogeneous patterns in the embedding space, conditioned on clean positive/negative agents stemming from the prediction space, without sacrificing the merits of confidence score, yielding better trad-off. We provide theoretical analysis to understand the mechanism of AgScore, and demonstrate its effectiveness by integrating it into three semi-supervised segmentation frameworks on Pascal VOC, Cityscapes, and COCO datasets, showing consistent improvements across all data partitions.

Beyond Confidence: Exploiting Homogeneous Pattern for Semi-Supervised Semantic Segmentation
Sui 19
Huayu Mai 23*
Wangkai Li
Yujia Chen 23
Naisong Luo?
Yuan Wang? Tianzhu Zhang? 23
Abstract
widespread applications such as embodied intelligence (Ev- eringham et al., 2010; Wu et al., 2022), autonomous driv- ing (Asgari Taghanaki et al., 2021), etc. However, its data- driven nature makes labor-intensive and time-consuming to gather massive pixel-level annotations as training data. For example, it takes around 1.5 hours to label single image Cityscapes (Cordts et al., 2016) with merely 19 classes. To alleviate the data-hunger issue, considerable works (Yang et al., 2022a; Sun et al., 2023c; Hu et al., 2021) have turned their attention to semi-supervised semantic seg- mentation task. Since only limited labeled data accessible, how to fully exploit large amount of unlabeled data to im- prove the model's generalization performance for robust segmentation is thus extremely challenging.
The critical challenge of semi-supervised seman- tic segmentation lies in how to fully exploit a large volume of unlabeled data to improve the model's generalization performance for robust segmenta- tion. Existing methods mainly rely on confidence- based scoring functions in the prediction space to filter pseudo labels, which suffer from the in- herent trade-off between true and false positive rates. In this paper, we carefully design an agent construction strategy to build clean sets of correct (positive) and incorrect (negative) pseudo labels, and propose the Agent Score function (AgScore) to measure the consensus between candidate pix- els and these sets. In this way, AgScore takes step further to capture homogeneous patterns in the embedding space, conditioned on clean positive/negative agents stemming from the pre- diction space, without sacrificing the merits of the confidence score, yielding a better trade-off. We provide theoretical analysis to understand the mechanism of AgScore, and demonstrate its effectiveness by integrating it into three semi- supervised segmentation frameworks on Pascal VOC, Cityscapes, and COCO datasets, showing consistent improvements across all data partitions.
To leverage unlabeled data effectively, consistency regu- larization (Sajjadi et al., 2016; Laine & Aila, 2016) and pseudo-labeling (Lee et al., 2013; Rizve et al., 2021) have emerged as mainstream paradigms for semi-supervised seg- mentation. Recently, these two paradigms are often assem- bled in the form of a teacher-student scheme (Wang et al., 2022a; Chen al., 2023). In this scheme, the teacher net- work, with weakly augmented view, generates pseudo labels to instruct the learning of the student model under a strongly augmented view. The success of this scheme relies on the correctness of the pseudo labels, as the quality the selected pseudo labels determines the upper bound of per- formance. Therefore, is crucial to design an appropriate criterion (referred scoring function) to detect and reject incorrect pseudo labels with low scores.
1. Introduction
Recently, confidence- bassed methods (Sohn et al., 2020; Mai et al., 2024b; Na et al., 2023; Yang et al., 2022b), such as UniMatch (Yang et al., 2022a), have dominated this field credited to their simplicity and competitive performance. The mainstream practice is attempt set hard thresh- old (e.g., 0.95) scoring function to filter out lexel- pseudo labels with low confidence in the prediction space. However, this scoring function tends not to be preferred as- cribed to the inherent trade-off between the true positive rate (TPR) and false positive rate (FPR), as illustrated in Figure I (a). On the one hand, a high confidence threshold ensures the quality of pseudo labels (low FPR), but discards numer- ous unconfident yet correct pseudo labels (unfavorably low
Semantic segmentation, which aims to predict a specific semantic class for each pixel, has achieved conspicuous achievements attributed to the recent advances in deep neu- ral network (Long al., 2015) in computer vision with
Equal contribution 'Shezzeen International Graduate School, Tsinghua University MMoE Key Laboratory of Brain-inspired In- telligent Perception and Cognition. University of Science and Technology of China "Nationa Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory. Correspondence to: Tianzhu Zhang <tzzhang@ ustc.edu.en>..
Proceedings of the 4dnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
10
Protictton
Illarrd Prodiction Accept-
Hight Vagrhaald
Pach Proditiienkiosee
treee foods
Coneet
Treimett
Correct
Lower TPR
Daraltood (Defallt
(c) Homogeeeouu Pattern in Pseudo-Labes
Lower FPR
TPR
LPPR
0.6
05-
Geed Treeds
Higher
Lover
Higher FPR
Lieeldence Uotibatiee for
Threshoff
Connuct vs. Isoomett Predktiies
Discard
022
Correct Predictions
Incorrect Prodictions
Confidence Distribution for Predictions
(b) Visualization of Pixel Predictions bicyele
(d) Trade-oft' between TPR and FPR
Figure 1. Motivation of the proposed AgScore. (a) Confidence distributions for correct and incorrect predictions, showing the inherent trade-off between TPR and FPR when using confidence thresholds. (h) Visualization of pixel emheddings predicted the class bicycle, demonstrating the homogeneous pattern where correct and incorrect pseudo-labels form distinct clusters. (c) Quantitative analysis of the homogeneous pattem, indicating that pixels with higher similarity to correct pseudo-label are more likely be correctly predicted. (d) AgSeore leverages the homogeneous pattern to achieve better balance. The experiments are conducted on the 1/16 partition of the Pascal VOC dataset (Everingham et al., 2010), assuming that the ground truth for unlabeled data is available solely for theoretical analysis.
TPR), compromising the model's capability. On the other hand, lowering the threshold encourages the utilization of more correct pseudo-labe pixels (high TPR) but inevitably enrolls erroneous pseudo labels that may mislead training (undesirably high FPR). Concurrently, several methods at- tempt to develop learning strategies to mine unlabeled data, including contrastive learning (Xu et al., 2022), disentan- gled supervision signals (Jin et al., 2022), and relation-wise consistency regularization (Mai et al., 2024b), etc. These methods operate in the embedding/feature space, which has been widely proven to have better tolerance for incorrect pseudo-labels compared to the prediction space. However, they are still confined to selecting pseudo labels in the pre- diction space (confidence scoring function). failing to tap into the potential of scoring pseudo labels from the embed- ding space itself. Then, the question naturally arises: How to explore the better scoring function from the embedding space beyond confidence?
different patterns encode the relative semantic comparabil- ity, which can be reflected by the differences in pattern consensus, and is from a different perspective than the con- fidence scoring function in prediction space. We further quantitatively investigate the static consensus of correct and incorrect labedo-label embeddings across different classes in Figure 1 (c). The results indicate that for any given pixel, there exists a higher probability of being correctly predicted if it exhibits a higher similarity to the set of correct pseudo labels compared to the set of incorrect pseudo labels. This provides hints for assessing the reliability of pseudo labels in the embedding space.
In this paper, we carefully design a new scoring function to filter out unreliable pseudo labels from a fresh perspective of the homogeneous pattern in embedding space beyond confidence, which is in line with the nature of the dense prediction task, for robust semi- -une-supeeeised semantic seg- mentation. The main idea is construct clean sets of correct and incorrect pseudo labels and leverage the homogeneous pattern derived from the noise-resistant embedding space to examine the consensus (similarity) differences between the candidate pixels and these sets in nuanced and detailed manner. In specific, we devise an agent construction strat- egy with two considerations: (1) For constructing a clean set of correct pseudo labels (referred to as positive agents), it is easy to select high-quality agents equipped with high confidence, considering that they have relatively simple pat- terns. (2) For constructing a high quality set of incorrect pseudo labels (negative agents), intuitively, these agents should resonate favorably with diverse semantic cues, con- sidering that incorrect pseudo labels originate from multiple classes corresponding to diverse patterns. We prepend an orthogonal selection strategy that maintains sufficient dif-
Our motivation is derived from an intuitive fact: pixels belonging to similar patterns tend to share homogeneous semantics compared to different patterns. For examplc, Figure 1 (b) showcases the embedding distribution of cor- rect and incorrect pseudo labels predicted as the class bi- cycle. evident that pixels from correct pseudo labels exhibit propensity to congregate together, forming ho- mogeneous patterns (i.e., green dotted circle). This phe- nomenon can also be observed, but with more nuanced paradigms, in pixels from incorrect pseudo-labels, Incor- rectly predicted classes often exhibit diverse patterns; for ex- ample, the classes motorbike and person, when erroncously classified as bicycle, correspond to two distinct patterns (red/blue dotted circle). We term this observation as homo- geneous pattern. This provides special bonus; in essence,
2

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Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
ferences from positive agents without compromising qual- ity. This strategy selects the most representative negative agents to cover broader semantic space and enhances the evaluation capacity. Now, we are prepared to propose the Agent Score function (AgScore) to score pseudo labels, which is positively correlated with the similarity between candidate pseudo labels and positive agents and negatively correlated with the similarity to negative agents. In this man- ner, AgScore takes step further capture homogeneous patterns in the embedding space, conditioned on clean pos- itive/negative agents stemming from the prediction space, without sacrificing the merits of confidence, yielding higher TPR and lower FPR (Figure 1 (d)). Furthermore, we provide a theoretical analysis to aid in understanding the mecha- nism of AgScore. This theoretical analysis translates into strong empirical performance when integrating AgScore into three semi-suuprrvied segmentation methods including FixMatch (Sohn et al., 2020), UniMatch (Yang et al., 2022a) and RankMatch (Mai et al., 2024b), achieving consistent im- provements for each across all data partitions in the Pascal VOC (Everingham et al., 2010), Cityscapes (Cordts et al., 2016), and COCO (Chollet, 2017) datasets.
tency regularization-based (Laine & Aila, 2016; Tarvainen & Valpola, 2017; Verma al., 2022; Xie et al., 2020) meth- ods leverage the smoothness assumption (Luo et al., 2018), encouraging the model to exhibit consistency when pre- sented with the same example under different perturbations. Notably, recent SSL methods (Sohn et al., 2020; Berthelot et al., 2019; 2021; Xu et al., 2021; Guo & Li, 2022) have demonstrated the synergy between consistency regulariza- tion and Pseudo- Interestingly, recent SSL methods have demonstrated the synergy between consistency reg- ularization and pseudo-labeling, combining the strengths of both approaches to achieve state-of-the-art performance. One prominent example is FixMatch (Sohn et al., 2020), which generates pseudo-labels from weakly augmented un- labeled images and uses them train the model on strongly augmented versions of the same images. This approach effectively combines the benefits of pseudo-labeling, which provides additional training targets, with the regularization effects of consistency training, which encourages the model to be robust to perturbations. This concise yet powerful approach has gained widespread adoption in recent SSL studies.
In this work, our contributions can be summarized as follows: (1) We propose novel Agent Score function (AgScore) that assesses the reliability of pseudo labels by exploiting the homogeneous pattern phenomenon, which is in line with the nature of the dense prediction task. AgScore measures the similarity between candidate pseudo labels and carefully constructed positive and negative agent sets to achieve better trade-off. (2) We design an agent con- struction strategy including confidence-based selection for positive agents and an orthogonal selection for nega- tive agents. This strategy ensures the quality and diversity of the agent sets, enhancing AgScore's evaluation capabil- ity. (3) We provide a theoretical analysis to understand the mechanism of AgScore. Extensive experimental results on challenging benchmarks show that integrating AgScore into existing semi-super segmentation frameworks yields consistent improvements.
Semi-supervised Semantic Segmentation. Benefits from the advances in deep neural network (Sun et al., 2021; Wang et al., 2022b; Mai et al., 2023; Luo et al., 2023; Wang et al., 2023d) and various kinds of semi-supervised semantic seg- mentation (SSSS) algorithms (Zhao et al., 2023b;a; Liang et al., 2023; Sun et al., 2023c; Na et al., 2023; Liu et al., 2023; Howlader et al., 2024; 2025; Mai et al., 2025; Sun et al., 2025) have been proposed based on the mature com- bination of Pseudo-labeling and consistency regularization. One notable work in this direction is UniMatch ( (Yang et al., 2022a), which takes into account the nature of semantic segmentation tasks and incorporates suitable data augmen- tations into the popular FixMatch framework. By carefully designing augmentations that preserve the semantic con- tent of the images while introducing meaningful variations, UniMatch has evolved into concise yet powerful SSSS baseline. The success of UniMatch has inspired numerous follow-up studies, which have further refined and extended this approach to achieve state-of- of-the--rt performance on various benchmark datasets. On top of these fundamental designs, motivated by representation learning, a series of works (Wang et al., 2023c;a; 2022c; Ma et al., 2023) have incorporated contrastive learning into SSSS, tailoring it to the characteristics of the dense prediction task. For example, Alonso et al. (Alonso et al., 2021) introduce a memory bank to store high-quality class features and perform positive-only contrastive learning. By maintaining a set of representative features for each class and using them as positive samples during contrastive learning, this approach can effectively capture the essential characteristies of different semantic categories and improve the model's discriminative power.
2. Related Work
Semi-supervised Learning. With the development of deep learning (Sun et al., 2023a;d:b;; Mai et al., 2024a: Chen et al., 2025), semi-supervised learning (Zhu, 2005; Grandvalet & Bengio, 2004; Oliver et al., 2018) (SSL) has attracted in- creasing attention for reducing annotation costs. Recent re- search can be summarized in two branches: pseudo-labeling and consistency regularization. Pseudo-labeling (Lee et al., 2013; Aascante-Bonille et al., 2021; Arazo et al., 2020; Zhang et al., 2021) methods involve training the model on unlabeled samples using pseudo-labele generated from the most to-to--ate optimized model. On the other hand, consis-
3

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Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
Similarly, Seg (Zhong et al., 2021) adopts pixel-level contrastive learning and introduces several negative sam- pling techniques to avoid the problem of sampling error. By carefully selecting negative samples that are likely to be informative and diverse, SC can learn more robust and generalizable representations for semantic segmenta- tion. Despite the significant progress made in SSSS research, there remain several challenges and open questions. One key challenge is the presence of noisy incorrect pseudo-labels, which can mislead the model and hinder its performance. Previous methods tend to use pseudo-label filtering strate- gies based on confidence scores. Instead of being trapped in focusing on confidence- panfidence-based scoring functions, we under- line that homogeneous pattern also worth investigating beyond confidence.
network) with low confidence:
Di = {
arg max fr (r(uug(z))); ignore_index,
(2)
otherwise '
where ctyy max fr (aug(a;)), represents the confidence of the teacher prediction for jth pixel and ry denotes the confidence threshold to exclude unreliable pseudo labels from training. As result, we can obtain the consistency regularization loss Creg as:
N
HW
1 N
Creg
Σ Lce ((ugg(((A)) j=1
HW
where Aug(-) means the strong augmentation. By impos- ing consistency regularization, the model can learn reliable information from unlabeled data. The overall loss of the commonly used teacher-student scheme is C = Luup + freg-
3. Method
While these methods have shown promising results, the scoring function they employ is often not favored due to its disregard for many correct pseudo labels with low con- fidence the early stages of training, raised by the strict threshold. As training progresses, they indiscriminately in- crease confidence in all pscudo labels, eventually recruiting over-confident erroneous pseudo labels into training and undermining the model's performance. In this paper, we carefully design a new scoring function to filter out unreli- able pseudo labels and retain reliable pseudo labels from a fresh perspective of homogeneous pattern. What follows, we detail the agent construction strategy first.
In this section, we first formulate the semi- cemi-supervised se- mantic segmentation problem as preliminaries and introduce the core idea of our AgScore from the perspective of ho- mogeneous pattern. Then we describe the details of the agent construction strategy and propose natural scoring function detect the reliability of the candidate pixel pre- dictions. Finally, the theoretical analysis is provided from the perspective of multi-label classification to demonstrate the effectiveness of our AgScore.
3.1. Preliminaries
Given a labeled set D' = {(a), (it.vv))) N and an unlabeled set n [=") j where N >> N semi-supervised semantic segmentation aims to train a segmentation model with limited labeled data and vast unlabeled data. The popu- lar teacher-student scheme consists of a teacher network fr and student network fs. The student network is guided by two sources of supervision, including the ground truth for the labeled data and the pseudo labels generated by the teacher network for the unlabeled data. The teacher net- work can either be identical to the student network or an exponentially moving average (EMA) version of the student network. Specifically, for the labeled data, the supervised loss Cuu can be formulated as:
3.2. Agent Construction
To enable the model to detect the reliability of candidate pixel predictions, we construct clean sets of correct and incorrect pseudo labels and leverage extra clues to examine the consensus (similarity) differences between the candi- date pixels and these sets. We operate at the level of pixel embeddings to enable the calculation of similarity between candidate pixels and the agent sets. Specifically, we ob- tain the pixel embeddings F E B for batch of unlabeled image {a::) ) before the classifier of the seg- mentation model and construct positive and negative agent sets from it. The overall agent construction process is shown in Algorithm 1.
HW
For the clean set of pixels with correct pseudo labels (re- ferred to as positive agents {af) N w=1). considering that they have relatively simple patterns, we randomly select N pixels from the top-1% 1% confidence pixels in F in blas-bbllanced manner and obtain {at (N
Csup
Σ loe (v))))(a))))) j=1
(1)
N
HW
where H and W represent the height and width of the input image, tee denotes the standard pixel-wise cross-entropy loss. For the unlabeled data, the dominant confidence- based methods set high threshold (e.g., 0.95) as a scoring func- tion to filter out pseudo labels (generated by the teacher
For the clean set of pixels with incorrect pseudo labels (re- ferred to as negative agents {a", 1M 1) considering that incorrect pseudo labels originate from multiple classes cor- responding to diverse patterns, we design an orthogonal

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Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
Table Quantitative results different SSI. methods on PASCAI. classic set. We report mloU (%) under various partition protocols
show the improvements A over the baseline.
ResNet-50
ResNet-101
Method
1/16
1/8
1/4
1/2 (732)
Full (1464)
1/16
1/8
1/4
1/2
Full (1464)
(92)
(183)
(366)
(92)
(183)
(366)
(732)
Sup-onnly
44.0
52.3
61.7
66.7
72.9
45.1
55.3
64.8
69.7
73.5
Pn (Xu et al., 2022)
70.0
74.7
77.1
78.5
80.7
GTA-Segrwwnnnr (Tin al., 2022)
70.0
73.2
75.6
78.4
80.5
ReCo (Liu et al., 2021)
64.8
72.0
73.1
74.7
AugSegevaaz1 (Zhao et al., 2023b)
64.2
72.1
76.1
77.4
78.8
71.0
75.4
78.8
80.3
81.3
NP-SemiSeggosszzy (Wang et al., 2023b)
65.7
72.3
75.7
77.4
DAWIN (Sun et al., 2023c)
68.5
73.1
76.3
78.6
79.7
74.8
77.4
79.5
80.6
81.5
DDFR (Wang et al., 2024)
74.9
78.0
79.5
81.2
81.9
PRCT (Xie al., 2024)
71.2
72.2
75.2
76.2
78.3
FixMatch (Sohn et al., 2020)
60.1
67.3
71.4
73.7
76.9
63.9
73.0
75.5
77.8
79.2
FixMatch+AggSooe
63.2
69.6
73.1
75.3
77.9
67.0
75.5
77.5
79.5
80.4
A1
+3.1
+2.33
+1.7
+1.6
+1.0
+-3.1
+.25
+-2.0
+1.7
+1.2
UniMatchewwwn (Yang et al., 2022a)
67.4
71.9
75.3
79.3
73.5
80.2
81.9
UniMoach+AAgSoore
69.4
73.6
76.8
79.4
80.0
75.6
77.2
80.1
81.3
82.6
A
+2.0
+1.7
+1.5
+1.4
+0.7
+2.1
+1.8
+1.4
+1.1
+0.7
RankMatchioveea (Mai al., 2024b)
71.6
74.6
76.7
78.8
80.0
75.5
77.6
79.8
80.7
82.2
RankMatth+.Aggoore
75.8
77.7
79.9
80.4
76.1
81.2
82.6
A
+1.5
+1.2
+1.0
+1.1
+0.4
+0.6
+-0.6
+0.5
+0.5
+0.4
selection strategy. Specifically, shown in Algorithm 2, we first select those pixels with very low confidence (bottom- 1%) and then incrementally build the negative agents set {a% Jm= sampled from the low-confidence pixels set such that new agent is maximally orthogonal (i.e., minimal co- sine similarity) to the agents already selected, starting with pixel features at random, where M denotes the number of negative agents. This strategy selects the most represen- tative negative agents to cover more semantic space and enhances the evaluation ability of reliability.
By modeling homogeneous pattern beyond confidence- based scores, noisy pseudo labels will be suppressed while the reliable ones will be highlighted, thus increasing their involvement consistency regularization based training. Finally. inspired by DivideMix (Li et al., 2020), we fit two-component GMM for S using the Expectation- Maximization algorithm. The Gaussian component with larger mean corresponds to the correct ones. For each pixel i, the probability of being correct is given by the pos- terior probability P(correct St). When the P(correct | S.) > 0.5, the pseudo-label of that pixel will be used the consistency regularization, instead of following the confi- dence thresholding strategy in Equation 2.
3.3. Agent Score
Now, we are prepared to propose the Agent Score function (AgScore) to assess the reliability of pseudo labels. Intu- itively, the AgScore is supposed to positively correlated with the similarity berween candidate pseudo labels and positive agents and negatively correlated with the similarity sum-softmax
4. Theoretical Analysis
To better understand how AgScore can facilitate pseudo- label selection, we provide a theoretical analysis from the perspective multi-label classification to demonstrate that the separability of correct and incorrect pseudo labels can be improved with the help of positive and negative agents.
negative agents. A natural design is function to check the proportion of the similarity between the pixel ff and positive agents {az N. in the union of positive agents and negative agents {al JV U ( LM
S(S) Σm=ı (cos(S )
(4)
Em= ecoa(S )
5

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Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
Table Quantitative results different SSI, methods on PASCAL, blender set. report mloU (%) under various partition protocols
show the improvements A over the baseline.
ResNct-50
ResNet-101
Method
1/16 (662)
1/8 (1323)
1/4 (2646)
1/16 (662)
1/8 (1323)
1/4 (2646)
Sup.-only
62.4
68.2
72.3
67.5
71.1
74.2
NouPSSn (Hu al., 2021)
77.2
77.6
78.1
PCR
(Xu et al., 2022)
78.6
80.7
80.8
GTA GTA-Seg (Jin al., 2022)
77.8
80.5
80.6
AugSegicvres (Zhao et al., 2023b)
74.7
76.0
77.2
77.0
77.3
78.8
Cr (Li al., 2023a)
75.0
77.1
77.7
76.8
79.1
79.9
NP- NP-SemiSegrowien (Wang ct al., 2023b)
73.4
76.5
76.7
DAW 21 (Sun et al., 2023c)
76.2
77.6
77.4
78.5
78.9
79.6
DDFP (4 (Wang et al., 2024)
78.3
78.8
79.8
PRCLucv24 (Xie et al., 2024)
77.9
79.1
79.9
FixMatchinaarreaaj (Sohn et al., 2020)
70.6
73.9
75.1
71.3
76.3
76.9
FixMatch+AAgScor
73.7
76.9
77.9
77.7
79.2
78.7
A
+3.1
+3.0
+2.8
+3.4
+2.9
+1.8
UniMatchewwwe (Yang al., 2022a)
75.8
76.9
76.8
78.1
78.4
79.2
UniMatch+AAgScrre
77.8
78.8
77.9
80.1
80.3
80.5
A
+2.0
+1.9
+1.1
+2.0
+1.9
+1.3
RankMatchhevssss (Mai al., 2024b)
76.6
77.8
78.3
78.9
79.2
80.0
RankMatch+AAgoore
78.4
79.1
80.0
80.3
80.6
A
+1.8
+1.3
+-0.9
+1.1
+1.1
+-0.6
4.1. Discrete Version of AgScore
soft counterpart, making our subsequent analysis practically meaningful.
For theoretical tractability, we consider discrete version of AgScore, denoted as:
Note that $ 1/(1+ Z), where = Y/X,, is monotonic function of Z. Studying Z simplifies derivations while still reflecting the underlying distributional differences between correct and incorrect pseudo labels. Therefore, we focus on the ratio Z = Y/X.
X X + Y
$
where X and Y represent the count of positive and negative agents exceeding a certain similarity threshold vh, respec- tively. Specifically, we define:
4.2. Distribution Approximation
We assume that the similarity scores 8,, = e {a)) fol- low an implicit distribution, leading to a Bernoulli random variable:
X
Σ [ss 2 31. Sn coo(s.aaaa))
M
[[ 2 16. Sm ccos(f.am))
8n 1[5 v Bernoulli(p),
In this context, X represents the number of positive agents whose similarity to pixel f exceeds the threshold, corre- sponding a multi-label classification task on the positive agents. Similarly, Y represents the count of the negative agents. When X is relatively large, the pixel exhibits higher similarity to the positive agents, making it more likely to be correct pseudo-label, which corresponds to a larger S, and vice versa. This characteristic indicates that the discrete version of Agoro behaves consistently with
where = P(s, 2 v) Similarly, 8m = [8m 2 + Bernoulligg). where = P(sm 2 ".) According to bino- mial approximation rules, we have the following lemma:
Lemma 4.1. For given pixel f. the counts of highly simi-
AAttually, since p/g varies with the agents, X and Y follow Poisson binomial distribution. We will prove in the Appendix that the subsequent derivations hold universally.
6

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Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
Table Quantitative results different SSI. methods on Cityscapes. We report mloU (%) under various partition protocols show the improvements A over the baseline.
5e
ResNet- 101
Method
1/16 (186)
1/8 (372)
1/4 (744)
1/2 (1488)
1/16 (186)
1/8 (372)
1/4 (744)
1/2 (1488)
Sup.-only
63.3
70.2
73.1
76.6
66.3
72.8
75.0
78.0
Neuuseuns (Hu et al., 2021)
74.0
75.8
76.2
2eutt (Xu al., 2022)
73.4
76.3
78.4
79.1
GTA- MAN-SegNNuutsst1 (Jin et al., 2022)
72.0
AugSegcvve 2s (Zhao et al., 2023b)
73.7
79.3
75.2
77.8
79.5
80.4
Co- Trainnecvvan et al., 2023b)
76.3
75.0
77.3
78.7
NP-SemiSegiicstenn (Wang et al., 2023b)
73.0
77.1
78.8
DAW (Neutts 23) (Sun al., 2023c)
75.2
77.5
79.1
76.6
78.4
79.8
80.6
DDFP(c (Wang et al., 2024)
77.1
78.1
79.8
80.8
PRCLnevssXXie el al., 2024)
73.4
77.0
77.9
80.0
FixMatchoooneeann (Sohn et al., 2020)
72.6
75.7
76.8
78.2
74.2
76.2
77.2
78.4
FixMatch+AgScore
76,5
78.3
78.9
80.4
77.6
79.0
79.2
79.5
At
+3.9
+2.6
+2.1
+2.2
+3.4
+2.8
+2.8
+1.1
UniMatchicvreen (Yang al., 2022a)
75.0
76.8
77.5
78.6
76.6
77.9
79.2
79.5
UniMatch+&ggcoore
76.8
78.4
78.8
79.2
78.3
78.4
79.9
80.2
f
+1.8
+1.6
+1.3
+-0.6
+1.77
+5.00
+7.0
79.5
RankMaathhoveesu (Mai et al., 2024b)
75.4
77.7
79.2
79.5
77.1
78.6
80.0
80.7
RankMatch+AAgSeore
76.6
78.6
79.7
79.9
78.2
79.6
80.5
81.0
At
+1.2
+00.9
+0.55
+0.44
+1.1
+1.0
+0.5
+0.3
lar positive and negative agents satisfy:
Let P1 and Pz represent the probabilities of pixels with correct and incorrect pseudo labels that share high similarity with positive agents, respectively. It follows that p1 > P2 naturally. Similarly, qI < q2 holds. Consequently, for correct pseudo labels, Z₁ ~ L.V(1, of), and for incorrect pseudo labels, Z2 [NV oz). With the distributions of correct and incorrect pseudo labels established, can now conduct quantitative analysis of their separability.
X
nn Binomial(N,,) 4 p)),
Y sm Binomial(M,.) 4 N(Mq, Mq g)),
4.3. Separability of Pseudo-label
(10)
To quantify the separability between correct and incorrect pseudo labels, analyze the false positive rate (FPR) at fixed true positive rate (TPR), i.e., X.
where S denotes converges in distribution.
Based on this lemma,
derive the distribution of Z:
Lemma 4.3. In the form of log-normal distribution, the FPR be represented as:
Proposition 4.2. The ratio Z Y/X X follows log-normal distribution:
FPR F2(FF (A))
2 4 LN(,,
(11)
where
= 1 erf
σ1 -erf-1(22)
11)
1)
#1 P12
02
V202
Mq Np
1-
1 1-
(13)
1 In
0² =
(12)
Mg
Np
where Fi and F2 denote the cumulative distribution func- tions of Zn and Zzz respectively.
Proof. See Appendix
Proof. See Appendix

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Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
Note that the number of positive agents N is closely related to TPR: increasing N would increase the probability of the existence of highly similar positive agents, thus increasing TPR. However, our metric F is defined at a fixed TPR. So we keep N constant and focus on the effect of M.
Cityscapes, respectively. We adopt stochastic gradient de- scent (SGD) optimizer with initial learning rate of 0.001 for PASCAL and 0.005 for Cityscapes. Polynomial Decay learning rate policy is applied throughout the whole train- ing. Further, when training a baseline integrated with our method, we use the same weak and strong augmentations as used by the corresponding baseline. The features used to construct the correlation consistency are extracted from the output of the ASPP module (Chen et al., 2017 7) and the chan- nel number is 256. We set the number of positive agents N = 64 and the number of negative agents M = 256 for all experiments. The model is trained for 80 epochs on PASCAL and 240 epochs on Cityscapes with a batch size of 8, using 8x RTX 3090 GPUs (memory is 24G/GPU).
Proposition 4.4. For fixed N, FPRx is a decreasing function of M, i.e.
ØFPR, 0M
e e-
1 0
V
erf (2X - 1)
0M
02
+
V2
0M
< 0.
(14)
Proof. See Appendix F.
5.2. Comparison with State-of-the-an Methods
We integrate AgScore into three representative SSL frame- works: FixMatch (Sohn et al., 2020). UniMatch (Yang et al., 2022a). RankMatch (Mai et al., 2024b), and evaluate its performance with both ResNet-50 and ResNet-101 101 back- bones under various partition protocols, following common practices in the field.
This suggests that increasing M improves the separability between correct and incorrect pseudo labels, which aligns with our observation that the patterns of incorrect pseudo labels are quite diverse. Therefore, in our experiments, we opted for a relatively large M (with M > _N) to ensure the effectiveness of AgScore. However, this does not imply that higher number of negative agents is always better. As the number of negative agents increases, the newly added negative agents will inevitably have semantic overlap with the positive sample agents, leading to reduced differences between positive and negative agents, which hinders the model's learning.
Results on PASCAL. Table I presents the comparative re- sults of AgScore with state-of-the-arr approaches on the PASCAL VOC classic set. Our method consistently en- hances the performance of all baseline SSL frameworks across all partition protocols and backbone networks. No- tably, under the most challenging 1/16 partition with only 92 labeled images, AgScore significantly boosts the perfor- mance of FixMatch, UniMatch, and RankMatch by 3.1%, 2.1%, and 0.6%, respectively, with ResNet- 101 backbone, demonstrating its powerful capability in leveraging unla- beled data under extremely label-scarce scenarios. Similar improvements are observed on the PASCAL VOC blender set, as shown in Table 2. AgScore consistently improves upon the baseline methods across all settings, with the most significant gains achieved under the 1/16 partition, further validating its effectiveness.
5. Experiments
5.1. Experimental Setup
Datasets: (1) PASCAL VOC 2012 (Everingham et al., 2010) is an object-centric semantic segmentation dataset, containing 20 object classes in the foreground and a back- ground class with 1,464 and 1,449 finely annotated im- ages for training and validation, respectively. Many re- searches (Chen et al., 2021; Hu et al., 2021) augment the original training set (i.e., classic) with additional 9,1 18 coarsely annotated images in SBD (Hariharan et al., 2011) to get blender training set. (2) Cityscapes (Cordts et al., 2016) is an urban scene understanding dataset consisting of 2,975 images for training and 500 images for validation. The initial 30 semantic classes are re-mapped into 19 classes for the semantic segmentation task. (3) COCO (Lin et al., 2014), composed of 118k/5k training/validatinn images, containing 81 classes to predict.
Results on Cityscapes. Table 3 shows the comparative results on the Cityscapes dataset. Our AgScore continues to enhance the performance of all baseline methods across various partition protocols and backbone networks. Under the most challenging 1/16 partition with only 186 labeled images, AgScore boosts the performance of FixMatch, Uni- Match, and Mank by 3.4%, 1.7%, and 1.1% respec- tively with ResNet- 101 backbone, verifying its robustness and generalization ability.
For fair comparison, we use ResNet-50/101 (He et al., 2016) pretrained on Im- ageNet (Krizhevsky et al., 2012) as the backbone and DeepLabv3+ (Chen et al., 2018) as the decoder. The crop size is set as 513 x 513 for PASCAL and 801 x 801 for
Implementation Details:
Results COCO. Table 4 summarizes that AgScore consistently achieves improvement across all partitions, demonstrating the effectiveness of integrating the proposed AgScore into the UniMatch framework. The improvement is particularly significant in the low-data regime, validating
8

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Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
AgScore's ability, especially when labeled data is scarce. The results align with the motivation and theoretical analy- sis, showcasing the potential of exploring scoring functions beyond confidence in the embedding space to address the in- herent trade-off between true positive rate and false positive rate in pseudo-label selection.
inevitably introduces considerable noise among these agents. This noise can adversely affect the quality of pseudo labels, resulting in sub-optimal performance. (2) The strategy of "Orthogonal" achieves the best results with a light computa- tional cost, which is in line with our design purpose, with light computational cost.
Qualitative Results. We compare the qualitative results of our method with different SOTA methods on the PASCAL dataset in Figure 2. AgScore shows more powerful segmen- tation performance in fine-grained details (e.g., the dogs on the bed and the man on horseback). With the help of mod- eling homogeneous patterns beyond confidence, AgScore exhibits superior abilities in most scenarios.
Trade-off of Pseudo Labels. As shown in Figure 3, in prac- tice, AgScore determines the criteria for selecting pseudo- labels by exploiting the homogeneous pattern phenomenon, which is in line with the nature of the dense prediction task and is conducive to model training. In this way. the model improves, benefiting from the effective probing of reliable pseudo-labels. And in turn, the positive distribution will he maximally separated from the negative agents. In a nut- shell, AgScore takes step further capture homogeneous patterns in the embedding space, conditioned clean pos- itive/negatiie agents stemming from the prediction space, without sacrificing the merits of confidence, yielding higher TPR and lower FPR.
5.3. Ablation Study and Analysis
To look deeper into our method, we perform a series of ablation studies on PASCAL classic set under 1/16(92) partition protocol with ResNet-50 to analyze our AgScore.
Analysis of AgScore. In Table 5, report the results of 1/16(92) and F164(1464) to clearly substantiate the effec- tiveness of our design. Note that, "baseline" denotes the reproduced results for UniMatch (Yang et al., 2022a). We have the following findings: (1) With the increase in the number of negative agents M, our AgScore achieves consis- tent performance improvements, which can be attributed to our design of orthogonal selection strategy that maintains sufficient differences from positive agents without compro- mising quality. This strategy selects the most representative negative agents to cover more semantic space and enhances the ability to evaluate reliability (index 2-5, index 6-7). (2) However, this does not imply that a higher number of neg- ative agents is always better. As the number of negative agents increases, the newly added negative agents will in- evitably have semantic overlap with the positive sample agents, leading reduced differences between positive and negative agents, which hinders the model's 's learning. This aligns with our theoretical explanation (index 4-5, index 7-8). (3) Within a certain range, increasing the number of positive samples does not lead to significant improvements in performance (index 2-5 vs.index 6-8). This is because is easy to select high-quality agents equipped with high confidence, considering that they have relatively simple pat- terns. However, when the number of samples exceeds a specific limit, it can harm performance due to the introduc- tion of incorrect but high-confidence agents, which affects the purity of the positive set (index 9-10).
6. Conclusion
In this paper, we proposed AgScore, a novel scoring func- tion that exploits the homogeneous pattern phenomenon to assess the reliability of pseudo labels for semi- semi-supervised semantic segmentation. AgScore measures the similarity between candidate pseudo labels and carefully constructed positive and negative agent sets, highlighting reliable pseudo labels while suppressing noisy ones. Extensive experimental results challenging benchmarks show the effectiveness.
A Acknowledgments
This work was partially supported by the National Defense Science and Technology Foundation Strengthening Program Funding (Grant 2023-ICIQ-I1-0119..
Impact Statement
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none of which we feel must be specifically highlighted here.
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Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
A. Algorithm
Algorithm Agent Construction.
Inputs: Pixel Embeddings F RRRxHWxC, Prediction P BB #Positive Agents N, #Negative Agents M Output: Positive Agents A" RRXxC, Negative Agents A E <RAx
1: K = Unique Classes P)
Determine the predicted classes set
2: Initialize empty sets F", ,
3: for each class E K do
4: FR (fi F
i(fi)) 1 k}
Extract pixels predicted class
5:
F {fi, E Fx
cj E top TK %]
6:
FR (fig F&
Ci bottom-
1 % [R]
7:
F" = PP=PUU U
8:
FT = F" F
9: end for
A" Random Select(F", .N)
11: A" BathoogonalSellcctionn M)
12: return A" A"
Algorithm 2 Orthogonal Selection Strategy.
Inputs: Pixel Embedding F E RM #Agents N Output: Agents A E R XC
1: A Random Random_Select((F 1)
Randomly initialized
2: for 1 to N 1 do
3: F = {ff & A}
Difference Set
4: S Cosine Siaine.Smillrrtt(FF A)
>> S E (HW
5:
S = Max(S, dim=1)
6:
index Argmin(S, dim=0)
> Min-Max Strategy
7: a Select(F, index)
8: A AAa
9: end for
10: return A
14

---

Beyond Confidence:
Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
B. More Results
In this section, we provide more experimental results due to space constraints in the main paper. These include:
(1) Detailed results on the COCO datasets (Table 4), showing consistent improvements from AgScore across different data partitions and baseline models. (2) Additional qualitative visualizations PASCAL VOC (Figure 2) that further demonstrate the advantages of AgScore, particularly in challenging regions.
(3) Ablation studies hyperparameter selection and the impact of different agent selection strategies (Tables 5 and 6), providing more insights to support the conclusions in the main text. (4) Analysis of trade-off between TPR and FPR comparing AgScore to confidence thresholding during training (Figure 3).
Image
Ground Truth
AEL
UniMatch
RankMatch
AgScore (Ours)
Figure Qualitative comparison with different methods. Note that significant improvements marked with yellow boxes.
Table 4. Quantitative results of different SSL methods COCO. improvements A over the baseline.
We report mloU (%) under various partition protocols and show the
Method
1/512 (232)
1/256 (463)
1/128 (925)
1/64 (1849)
1/32 (3697)
Sup.-only
22.9
28.0
33.6
37.8
42.2
PseudoSegiArii (20] (Zou al., 2020)
29.8
37.1
39.1
41.8
43.6
PC2Segacevin (Zhong et al., 2021)
29.9
37.5
40.1
43.7
46.1
CISC-RIPAMEER (Wu et al., 2023)
32.1
40.2
42.2
UniMatchhcvwery (Yang et al., 2022a)
31.9
38.9
44.4
48.2
49.8
UniMatch+Aggcore
33.9
40.6
45.7
49.7
51.2
1
+2.0
+1.7
+1.3
+1.5
+1.4
15

---

Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
Table Quantitative results of the effectiveness of AgScore.
Variants
mloU(92)
mloU((1464)
Baseline
67.4
79.3
M=64
68.5
79.6
M=128
69.0
79.7
N=64
4
M-256
69.4
80.0
5
M-512
68.1
79.4
M=128
69.2
79.6
N=128
M=256
69.5
80.2
M=512
67.8
79.6
M=256
68.1
79.2
N=256
10
M=512
67.7
79.0
Table 6. The effectiveness of agent construction strategy.
Method
mloU(92)
Flops (G)
Baseline
67.4
17.4
Uniform
67.7
17.5
Bottom
68.0
17.5
Orthogonal (ours)
69.4
18.7
0.5
0.900
Posttive
0.875
JFPR Positive
0.025
0.800
AgSccrn
AgSccon Candibeoaad.00
O. 95
20
50
80
10
70
Training Esochs
Training Epochs
(a) TPR
(b) FPR J
Figure Comparison AgScore Confidence-O.95 terms of (a) True Positive Rate (TPR) and (b) False Positive Rate (FPR) over training epochs.
16

---

Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
C. More General Case of X & Y
the previous derivation, we treated P(sn 2 v) as equal to constant p. allowing us to sum independent and identically distributed Bernoulli random variables obtain a binomial distribution, which we then approximated as Gaussian distribution for the sake of convenience in subsequent derivations. However, in reality, Pn P(s,, > +)) should vary with the agents and the sum follows the Poisson binomial distribution. In this section, will demonstrate that, under more general conditions, X and Y can still be approximated as Gaussian distributions. This implies that, despite the simplifications made for the convenience of derivation, the subsequent derivations remain valid and can effectively reflect the separability between correct and incorrect pseudo-labels.
Formally, rewrite X Σ 8n, where 8,
18n 2 vb] Bernoulli(pw.. Pr. = P(,n 2 15) is easily shown that:
[5 =
Pm
Var[S,]] = Pr -0-mp)) Pn).
(15)
N
N Var[X] = Σ
B[
Pai
Pm (1(1-110 - Pr).
(16)
n=1
n=1
Since 8 can only take values 0 and have:
El|s, - [[(s,]] 2+
= 11 Pn 12+6 P( = 10 - pnl 22+ p2l2+0 P(sn = 0)
= (1 [pn) 22 Prr + PP (1 Pr))
(17)
<
(Var [a][X]) 245
Pn (1 paa)) 245
(18)
where (in p.)). Following Equation 17 and Equation 18, we have:
1
2N
0
Ells, E[s,]|
2400
(19)
(VarlX])
N 7
244
N
Obviously, X satisfies the Lyapumov condition:
Blis, E[
+
N - xc,
(20)
(Var
Based on the Lyapunov central limit theorem, we have:
X S N(E VarlX]).
(21)
Similarly, Y also approximates a Gaussian distribution.
17

---

Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
D. Proof of Proposition 4.2
Rewrite the Proposition:
Proposition D.I. The ratio Z = Y/X follows a log-normal distribution:
2 C.N,,,
(22)
where
Mq Np
0² 1 9
+ 1-p
In
(23)
My
Np
Proof. Taking the logarithm on both sides, we have:
In Z In Y X.
(24)
To apply the Delta method, we note that since and are approximately normally distributed, we can compute the mean and variance of in X and In Y.
For positive random variable X that is approximately normally distributed, use the Delta method to derive:
Eln X] In B[X],
and
Var[X]
Var X]
(
Applying this to and Y, we have:
For X:
Eiin X] = In(Np),
VarlIn X] (1- p)
Np
For Y:
E(In Y] In ln(Mg)
9)
VarlIn YJ ~ Mg
Since the difference two normally distributed variables is also normally distributed, follows that In normally distributed. Thus, Z is log- -ormaalyy distributed:
Z S CN(,,,7),
(25)
where
Mq Np
0 1 9
1- p Np
p
(26)
Mg
18

---

Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
E. Proof of Lemma 4.3
Rewrite the Lemma:
Lemma E.I. In the form of log-normal distribution, the FPRx can be represented as:
FPR^ F2(FT
1
σ1 erf
(2x - 1) M1 H2
(27)
1 + erf
2
02
V202
where F1 and F2 denote the cumulative distribution finction of b1 and Zy, respectively.
Proof. Based on Proposition 4.2,
For correct pseudo-labels, Z1 LN(U1,0f) where:
Mqn
of 1- 91
1 1- P1 Np1
= In Np
+
(28)
Mgi
For incorrect pseudo-labels, Z2 LN( of), where:
Mg²
1- 92
1- P2
122 = In
o
+
(29)
Np2
Mgz
Np2
The cumulative distribution function F(x) and inverse function F F-1(A) can be represented as:
1 F(z;u,o²²) - erf(
x - M
erf(
V20 ]
(30)
(x = exp(u 22eer (22--1) 1)}
(31)
Since P1 P2 and 91 92, we have 14 U2z For given TPR X, the threshold can be derived:
F= (X;,, exp(p 22 1)}
(32)
For given threshold r, the FPR can be obtained:
F(T:)2,03)
_ + erf(
1
In - #2
V202
(33)
1
1 erf
01 erf
erf
(2) 12)
A1 H2 V202
2
02
i.e.,
FPRA
1
1 1 erf
01 erf
1(2A 1)
11 #2
(34)
2
02
V202
19

---

Beyond Confidence: Exploiting Homogencous Pattern for Semi-Supervised Semantic Segmentation
F. Proof of Proposition 4.4
Rewrite the Proposition 4.4:
Proposition F.1. For a fixed N, FPRs is decreasing functioon of M, i.e.,
OFPR 0M
e
erf 12X 1) 0
1
0 11
02
V
QM
/ 0M
(35)
< 0.
Proof. We start from the formula:
FPRA-
1
f
1 erf
01 erf
(2) 1)+
M1 u2
(36)
2
02
v 22
The error function ex(( is defined as:
/ c - dt.
(x)
(37)
0
Next, we differentiate FPR, with respect to M using the chain rule. Let t
orr 1) +
Mi M1-
02
\/203
OFPR
e --
01
erf- (2X - +1) 11 - P2
0M
VT
0M
02
220
(38)
e-
0
1 θ 14
erf
1(2
1)
02
V
0M
V2 0M
We obtain:
OFPR
e --
001 1)
(p1
122
1
erf
erf
(2X
02
+
(39)
VT
0M
V2
δM
2
Next, we analyze the derivatives of these components.
Regarding i and o2. σi and d2 represent the standard deviations of correct and incorrect pseudo-labels, respectively, As the number of negative agents M increases, d2 increases due to the increased diversity of incorrect pseudo-labels, leading aca aM
decrease in Øi This implies .2
2. Regarding μ41 P2: P1 and #2 the means associated with correct and incorrect pscudo-labels, respectively. With an increase in M, #2 may increase, while μr remains relatively stable, resulting in 0(g ((1)))2)
0.
aM
Combining these parts, we conclude:
@FPR^
θM < 0.
Thus, FPRA decreases as M increases.
20


Reviews:
[{"review_id": "qL71PHymx5", "reviewer": "Reviewer_ZpSC", "summary": "The authors introduce “AgScore” (Agent Score), a scoring function to filter out unreliable pseudo labels at the pixel level in order to improve the performance of existing semi-supervised semantic segmentation (SSSS) methods. Unlike prior work that primarily relies on high-confidence thresholding in the prediction (logits) space to decide which pseudo labels to use, this paper focuses on the phenomenon of “homogeneous pattern” in the feature/embedding space. Specifically, the authors first construct two sets of agents: positive agents (high-confidence, presumably correct pseudo labels) and negative agents (low-confidence, presumably incorrect pseudo labels). chosen through an orthogonal selection strategy ensuring semantic diversity. Then, a function computes each unlabeled pixel’s similarity to these two agent sets. If a pixel is more similar to the positive agent set than the negative agent set, the method deems its pseudo label more reliable; otherwise, it is filtered out. Empirically, integrating AgScore into existing SSSS frameworks such as FixMatch, UniMatch, and RankMatch consistently improves segmentation performance across popular benchmarks like Pascal VOC, Cityscapes, and COCO.", "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": "Thanks for taking the time to share your comments in the review assessment, as well as for acknowledging the **well-supported core claim**, **simple but effective idea**, **systematic experimental validation**, and **insightful theoretical analysis**. We provide a detailed point-by-point response to your comments. \n\nNote that the following **link** refers to\nhttps://anonymous.4open.science/r/AgScore/Rebuttal.pdf\n\n---\n\n**Q1**: Robustness of AgScore in handling edge cases. \n\n**A1**: We appreciate your insightful question. Indeed, edge cases like **large domain shift**, **strong multi-class overlap** or **severely noisy unlabeled data** pose significant challenges to semi-supervised learning. To mitigate these limitations, our orthogonal selection strategy for constructing the negative agent set aims to select representative and diverse negative samples, covering a broader semantic space of incorrect pseudo-labels. This helps to some extent in handling domain shift, class overlap and noisy data. However, we acknowledge that in extremely severe edge cases, the baseline methods may struggle to produce effective predictions, which could hinder the selection of reliable agents and the evaluation of pseudo-labels. \n\nIn this paper, we only focus on the conventional semi-supervised setting, where the baseline methods generally yield effective predictions that support our approach. Your valuable suggestion inspires us to explore extending our method to extreme cases in future work, which is crucial for enhancing the robustness of semi-supervised segmentation. \n\n---\n\n**Q2**: Robustness of AgScore when the teacher model is underfitted.\n\n**A2**: Indeed, the key to the success of our method lies in selecting reliable positive and negative agents. As shown in Fig. 9 in **link**, we record the precision of the selected positive and negative agents after each training epoch. The agents are selected based on the top-1% and bottom-1% confidence scores, respectively. Notably, even after just the 0-th epoch, the precision of both positive and negative agents is already satisfactorily high. This suggests that the teacher model can provide reliable agents for AgScore almost right from the start of training. Consequently, to maintain the simplicity of AgScore, we do not employ any specific warm-up strategies, as the experiments demonstrate its effectiveness without such strategies. We concur that carefully integrating warm-up strategies could potentially lead to better performance, and we leave this as a direction for future investigation.\n\n---\n\n**Q3**: Typo and \"AugSeg\" misuse.\n\n**A3**: Thanks for your meticulous review and valuable feedback. We apologize for the oversight in mistakenly referring to \"AgScore\" as \"AugSeg\" in the Experiments section. We will carefully review the manuscript, correct these errors, and improve the clarity of our expressions.\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 core claim that incorporating feature-space similarity (via positive and negative agents) enables more precise filtering of pseudo labels than standard confidence-thresholding alone is well supported by the experiments conducted integrating AgScore into three well-known baselines.", "methods_and_evaluation_criteria": "* Evaluation was done on three standard SS datasets (Pascal VOC, Cityscapes, COCO) using various data partition protocols following the practices in the field. \n* AgScore is integrated into three different SS frameworks with improved performances in all cases, proving its effectiveness. \n* Ablation studies (Tables 5, 6 and Figure 3 from the supplementary) support the design choices.", "theoretical_claims": "I have not properly assessed the correctness of the theoretical claims and the proofs, but I confirm I have looked over them, and nothing evidently stands out as wrong.", "experimental_designs_or_analyses": "These are sound as they follow the general practices in the field.", "relation_to_broader_scientific_literature": "* The authors place their work in the context of existing semi-supervised segmentation approaches that rely on teacher-student frameworks, including FixMatch and others. They also connect their approach to contrastive learning in the feature space or methods that propose robust pseudo-label selection. \n* The authors also relate their approach to relevant classical SSL ideas (like consistency regularization and pseudo-label thresholding).", "essential_references_not_discussed": "Overall, references to major prior SSSS methods (e.g., FixMatch, MeanTeacher, ST++, ReCo, etc.) are included. Even very recent ones like RankMatch (2024).", "other_strengths_and_weaknesses": "• Strengths:\n  - The agent construction idea is simple but effective and can be integrated with minimal overhead into multiple SSSS frameworks.\n  - The experimental validation systematically shows improvements.\n  - The theoretical discussion is a welcome addition.\n  \n• Weaknesses:\n  - The approach is based on carefully sampling from the top/bottom confidence. Though the paper includes ablations, there might be edge cases where strong multi-class overlap or severely noisy unlabeled sets pose challenges.\n  - The approach’s success depends on the assumption that extremely high-confidence pixels are reliably correct. This is usually true for well-trained teacher networks but might occasionally fail early in training or with domain-shift data.", "other_comments_or_suggestions": "I recommend the authors have a closer look at their paper, there are still some typos to deal with and paragraphs (\"trad-off\") that need rephrasing (Page 1, column 2, L45-48) to improve clarity (Table 1 mentions bolded results, but none of them are bolded). But the most alarming thing is the confusion the others make between AgScore and AugSeg (a method published and referenced in CVPR 2023), which the authors mentioned as their own when here the authors should have referred to AgScore. I treated this as an error; otherwise, using \"Our AugSeg\" breaches the double-blind submission restriction.", "questions_for_authors": "What it's currently missing from the experiments is a proper assessment of AgScore's robustness when the teacher model is underfitted in the early stage of training. Are there any warm-up strategies to ensure reliable positive agents from the start? Also, I am curious to know this, especially for domain shifts in a semi-supervised setting. Does the \"top confidence = correct\" assumption still hold there? Any insights from the authors are welcomed.", "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": "lEd9VDIlER", "reviewer": "Reviewer_zrAv", "summary": "This study focuses on semi-supervised semantic segmentation which struggles to effectively use unlabeled data due to challenges in balancing true and false positives when filtering pseudo labels. This paper introduces an agent construction strategy and the Agent Score function (AgScore) to better identify correct and incorrect pseudo labels by leveraging patterns in the embedding space. AgScore is theoretically analyzed and shown to enhance segmentation performance across multiple frameworks and datasets, with code and models provided for future research.", "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": "Thanks for taking the time to share your comments in the review assessment. We provide a detailed point-by-point response to your comments.\n\n**link** refers to https://anonymous.4open.science/r/AgScore/Rebuttal.pdf\n\n---\n\n**Q1:** More Evidence.\n\n**A1**:\n\n- Claim 1: To demonstrate the generalization of the homogeneous pattern phenomenon, we conduct experiments and visualizations on both Pascal VOC and Cityscapes (1/16 partition) using ResNet-50. \n\n    (1) Single-category visualizations (Fig. 5 & 6 in link) highlight the top-4 most frequently predicted classes in each dataset. These results show that pixels with similar patterns within a class tend to share homogeneous semantics, while those from different classes exhibit distinct patterns. This supports our claim that “pixels with similar patterns tend to share homogeneous semantics compared to different patterns.” \n\n    (2) Multi-category visualizations offer a global perspective: correctly predicted pixels (darker) and incorrectly predicted ones (lighter) form well-separated clusters in the embedding space, further validating the link between visual patterns and semantic consistency.\n\n- Claim 2: To clarify how Fig. 1(a) is generated, we provide a step-by-step explanation in Fig. 7 (link).\n\n    Step (1) shows the confidence distributions of correct and incorrect predictions on Pascal VOC (1/16 split). \n\n    Step (2) defines true/false positive rates (TPR/FPR), while Steps (3) and (4) illustrate the trade-off between them under different confidence thresholds. Ground truth for unlabeled data is used here only for analysis.\n- Claim 3: In Fig. 1(c) (or Tab. 7 in link), we quantitatively measure the embedding similarity between pixels with correct and incorrect pseudo labels. For each class, we compute the similarity between each predicted pixel and the sets of correct/incorrect pixels, average the results, and normalize them class-wise.\n\nThe results show that correctly predicted pixels have much higher average similarity to other correct pixels (0.925) than to incorrect ones (0.075), and vice versa. These findings provide numerical evidence that aligns with the qualitative observations in Fig. 1(b).\n\n---\n\n**Q2**: More Experiments.\n\n**A2:** \n- Exp 1: Agent Selection Effectiveness & Class Imbalance \n\n    (1) We evaluate the top-1% confidence-based selection strategy on Pascal VOC (1/16 split, ResNet-50). As shown in Fig. 8 (link), the high precision of selected pixels for both positive and negative agents confirms the effectiveness of our confidence-based strategy. \n\n    (2) Pascal VOC itself exhibits severe class imbalance (e.g., background has >200× pixels than bicycle). Cityscapes and COCO show even larger head-to-tail ratios (>400 and >10,000). Despite this, our agent selection strategy (Algorithm 1), which samples clean examples in a class-balanced manner, remains robust—consistently improving performance across datasets (Tables 1–4). \n    \n    (3) As for using labeled GT to construct agents, we’ve tested this on Pascal VOC (92 labeled images) with UniMatch as baseline (Tab. 10 in link). Due to the known distribution gap between labeled and unlabeled data (as noted in DAW, SoftMatch), agents built from GT-labeled pixels underperform compared to our confidence-based method.\n\n- Exp 2: Design Choices for AgScore \n\n   (1) Incorporating Negative Agents: Using only positive agents (Row 1) omits key contrastive signals, leading to inferior performance. Adding negative agents (Rows 2/3) in a proportional form introduces nonlinearity that normalizes and stabilizes similarity scores, enhancing label separability. \n\n   (2) Exponentiation for Score Scaling: Applying the exponential function to cosine similarity (Row 3) amplifies score differences, further improving performance—aligning with our theoretical motivation.\n\n- Exp 3: Orthogonal Selection Strategy \n\n    (1) Our orthogonal selection strategy is tailored for negative agent construction, selecting diverse, low-confidence samples to ensure agent purity (Fig. 8). \n\n   (2) This enhances semantic coverage and improves the separation between correct/incorrect predictions, supporting our theoretical design. \n\n    (3) In practice, AgScore serves as a general plug-in compatible with various methods (e.g., with different loss functions), making our orthogonal selection both reasonable and flexible.\n\n- Exp 4: Sensitivity to Selection Ratio Experiments in Fig. 9 (link) show that raising the pixel selection ratio to 2% reduces accuracy. Our 1% threshold is thus a simple yet effective default. While not heavily tuned, this setting offers room for further optimization based on dataset characteristics.\n\nWe will include this analysis in the revised version.\n\n---\n\n**Q3:** Theory.\n\n**A3:** Please refer to the answer **A3** of reviewer bo7S.\n\n---\n\n**Q4:** Others.\n\n**A4:** We will improve the typos and add more discussions due to space limitations.\n\n---\n\nWe hope our response can resolve your concern. Have a nice day!", "claims_and_evidence": "No. The following claims made in the submission are not supported by clear and convincing evidence,\n\n- Claim1: \"pixels belonging to similar patterns tend to share homogeneous semantics compared to different patterns.“  Reason: The author uses the example in Figure 1(b) to demonstrate the homogeneous pattern, but this example is not representative and is likely the result of careful selection. More categories and a more comprehensive demonstration are needed to substantiate this claim. Moreover, although the features are distinctive, it is unconvincing that they are all classified as bicycles. The author needs to visualize the features of all relevant categories in the dataset together to enable reviewers to better assess whether Figure 1(b) is reasonable.\n\n- Claim2: \"However, this scoring function tends not to be preferred ascribed to the inherent trade-off between the true positive rate (TPR) and false positive rate (FPR), as illustrated in Figure 1 (a).\" Reason: It is unclear how Figure 1(a) was generated.\n\n- Claim3: \"The results indicate that for any given pixel, there exists a higher probability of being correctly predicted if it exhibits a higher similarity to the set of correct pseudo labels compared to the set of incorrect pseudo labels.\" Reason: The same as Claim1, the evidence from Figure 1 is not convincing.", "methods_and_evaluation_criteria": "This paper introduces an agent construction strategy and the Agent Score function (AgScore) to better identify correct and incorrect pseudo labels by leveraging patterns in the embedding space. However, the experiments lack sufficiently comprehensive ablation studies to evaluate the proposed method. For example,\n\n- Why \"we randomly select N pixels from the top-1% % confidence pixels in F in a class-balanced manner\"? Any experiments to show the effectiveness of this method? How would the method handle a long-tailed distribution dataset? Additionally, is it possible to leverage the known ground truth (GT) data to construct this? I believe this part lacks corresponding experiments.\n\n- Also, the design of section 3.3 lacks corresponding experiments. \n\n- Is the designed orthogonal selection strategy reasonable, and would different training methods affect this approach (e.g., using different loss functions or different pixel category determination algorithms)?\n\n- Whether the pixel selection strategy in the \"Agent Construction\" section is truly reasonable needs to be validated through more quantitative and qualitative experiments. For example, how do the distributions of predictions and ground truth (GT) in the top-1% and bottom-1% compare, and is there a difference compared to the top-2%? Is it necessary to rely on different datasets to rigorously control this parameter, and so on?", "theoretical_claims": "I have reviewed it, and while there are no major issues, it seems that these validations do not address the core problems present in the paper.", "experimental_designs_or_analyses": "Refer to \"Methods And Evaluation Criteria\".", "relation_to_broader_scientific_literature": "It is uncertain, as it is unclear whether the algorithm has limitations, such as being applicable only to specific datasets or particular domains.", "essential_references_not_discussed": "Perhaps, adding some more recent works in the related work section and including a dedicated section to discuss approaches for better pseudo-label planning would be beneficial.", "other_strengths_and_weaknesses": "Algorithm 1 and Algorithm 2 can be fully merged.", "other_comments_or_suggestions": "There are basically no typos in the paper that affect reading comprehension.", "questions_for_authors": "Refer to \"Methods And Evaluation Criteria\" and \"Claims And Evidence\".", "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": "OGrHiapzqc", "reviewer": "Reviewer_bo7S", "summary": "This paper focuses on the confidence-based scoring functions in the semi-supervised semantic segmentation task. An agent construction strategy, aka., AgScore, is proposed to build clean sets of correct and incorrect pseudo labels. Experiments on three datasets show performance improvements in semi-supervised semantic segmentation.", "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": "Thanks for taking the time to share your comments in the review assessment, as well as for acknowledging the **new perspective**,  **well-supported motivation** and  **clear idea**. We provide a detailed point-by-point response to your comments. \n\nNote that the following **link** refers to\nhttps://anonymous.4open.science/r/AgScore/Rebuttal.pdf\n\n---\n\n**Q1**: Concept of Agent.\n\n**A1**: Sorry for not providing you with an intuitive understanding of the term \"agent\". In our work, \"agents\" refer to the sets of pixels that are considered to have correct or incorrect pseudo-labels, serving as a **bridge** for evaluating the correctness of the pseudo-labels for the remaining pixels. This is why \"agents\" are named.\n\n---\n\n**Q2**: Independence Assumption of X and Y.\n\n**A2**: We assume that X and Y are independent for the convenience of subsequent theoretical derivations. In practice, we first select the top-1% and bottom-1% confidence pixels and then randomly/orthogonally sample positive/negative agents from them. This is done to **ensure that the independence assumption** holds to the greatest extent. To make the theoretical derivation more general, we even consider the case where the sampled agents are not identically distributed, i.e., the Poisson binomial distribution (Sec. C in the Appendix). Overall, our assumption is mild and supported by implementation.\n\n---\n\n**Q3**: Value of our Theoretical Analysis.\n\n**A3**: Our theoretical analysis is closely intertwined with our method and experiments. As mentioned above, the agent selection strategy employed in our method is designed to ensure the independence assumption in the theoretical analysis. Moreover, the conclusion drawn from our theoretical analysis, \"increasing M enhances the separability between correct and incorrect pseudo-labeled pixels,\" is also validated by Tab. 5 in our experiments. At the same time, the experiments also demonstrate that when M becomes excessively large, the performance degrade, since the independence assumption breaks down.\n\n---\n\n**Q4**: Training and Evaluation Efficiency.\n\n**A4**: Thanks for your valuable suggestion. Notably, our AgScore is only involved in the selection of pseudo-labels during training and **does not introduce any additional cost during evaluation**. To further quantify the efficiency impact of AgScore, we report the GPU memory and training time of AgScore and baseline UniMatch under the same setting (Pascal VOC classic, 92 partition, cropsize=513, batchsize=8, ResNet50).\n\n|          | mIoU | GPU Memory (G) | Training Time (h) |\n| :------: | :--: | :------------: | :---------------: |\n| UniMatch | 67.4 |      44.2      |       23.6        |\n| AgScore  | 69.4 |      46.9      |       25.3        |\n\nWe observe that AgScore brings a significant performance improvement at the cost of a slightly increased memory consumption and an acceptable increase in training time.\n\n---\n\n**Q5**: Assumptions in Theoretical Analysis.\n\n**A5**: As shown in Fig. 1(c), we compute the average similarity between pixels corresponding to correct and incorrect pseudo-labels. It is evident that the similarity between pixels with correct pseudo-labels is greater than that between pixels with correct and incorrect pseudo-labels, i.e., $p_1 > p_2$ holds statistically. Similarly, $q_1 < q_2$ also holds statistically.\n\n---\n\n**Q6**: Semantic Overlap in Negative Agents.\n\n**A6**: As you mentioned, when M becomes sufficiently large, there will be semantic overlap among negative agents. In this case, the most harmful negative agents are those that have semantic overlap with positive agents, as this violates the assumption that $p_1 > p_2$. Consequently, $\\mu_1 - \\mu_2$ increases, leading to a larger $\\text{FPR}_\\lambda$ (as shown in Lemma 4.3), which in turn degrades the separability between pixels with correct and incorrect pseudo-labels.\n\n---\n\n**Q7**: More Visualization of Results.\n\n**A7**: As shown in **link** Fig. 4 and 5 , we supplement additional t-SNE visualizations for more classes on the Pascal and Cityscapes datasets. It can be observed that all classes clearly exhibit the Homogeneous Pattern. Additionally, as shown in **link** Fig. 7, the precision of the positive and negative agents obtained using simple top/bottom confidence is satisfactory. Therefore, leveraging relatively clean positive and negative agents, we are able to distinguish between correct and incorrect pseudo-labels.\n\n---\n\n**Q8**: Impact of Negative Agent Selection.\n\n**A8**: In Tab. 6 of Appendix, we explore various strategies for negative agent selection strategy, including \"Uniform\" represents uniformly sampling negative agents from the candidate set, \"Bottom\" represents sampling negative agents in ascending order of confidence. We observe that our strategy of \"Orthogonal\" achieves the best results with a light computational cost.\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 motivations from the \"homogeneous pattern\" is clearly shown in Fig.1. \n\nWhy the concept o Agent introduced here? It is not clear what is the relationships between \"Agent\" and the pseudo labels. The reviewer believes the name is somehow misleading.", "methods_and_evaluation_criteria": "The manuscript includes a substantial amount of theoretical analysis that appears to be tangential to the core focus of the study. Specifically, the exact operational mechanism of the 'agent score' metric remain unclear. The reviewer finds it challenging to identify how this metric is computed or applied in practice.", "theoretical_claims": "1. In the theoretical analysis, please discuss the independence assumption of X and Y and analyze its possible impact.\n\n2. Suggestions: the theoretical analysis should supplement the discussion of the problem that the increase of negative sample agents may lead to semantic overlap, and analyzes its impact on separability.", "experimental_designs_or_analyses": "The experimental results mainly focus on the performance, while neglecting the training and evaluation efficiency, there should be a table or a subsection to discuss about this.", "relation_to_broader_scientific_literature": "A new perspective on the pseudo label selection in semi-supervised semantic segmentation.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "- **Formula Derivation**:\n  - By analyzing the distribution of \\( Z = \\frac{Y}{X} \\), it is proven that increasing the number of negative agent samples \\( M \\) can improve the separability between correct and incorrect pseudo-labels.\n  - The relationship between FPR (False Positive Rate) and \\( M \\) is derived, proving that \\(\\frac{\\partial FPR}{\\partial M} < 0\\).\n\n- **Issues**:\n  - The derivation assumes \\( p_1 > p_2 \\) and \\( q_1 < q_2 \\), but no experimental or theoretical support is provided to justify these assumptions.\n  - The derivation does not consider that increasing \\( M \\) may lead to semantic overlap among negative agent samples, thereby affecting separability. This point is mentioned in the experimental section but is not discussed in detail in the theoretical analysis.", "other_comments_or_suggestions": "There can be some visualization results to prove the effectiveness of the proposed method, such as TSNE, etc.", "questions_for_authors": "1. **Training and Evaluation Efficiency**:\n   - The experimental results primarily focus on performance metrics. Could the authors provide a table or subsection discussing the training and evaluation efficiency of the proposed method? This would help readers understand the computational cost and scalability of the approach.\n\n2. **Assumptions in Theoretical Analysis**:\n   - The derivation assumes \\( p_1 > p_2 \\) and \\( q_1 < q_2 \\). Could the authors provide experimental or theoretical evidence to support these assumptions? This would strengthen the validity of the theoretical analysis.\n\n3. **Semantic Overlap in Negative Agents**:\n   - The derivation does not consider the potential semantic overlap among negative agent samples when \\( M \\) is increased. Could the authors elaborate on how this might affect the separability and overall performance of the method?\n\n4. **Visualization of Results**:\n   - Could the authors include visualization results, such as t-SNE plots, to demonstrate the effectiveness of the proposed method? Visual evidence could provide additional insights into how the method distinguishes between correct and incorrect pseudo-labels.\n\n5. **Impact of Negative Agent Selection**:\n   - How does the selection strategy for negative agents (e.g., orthogonal selection) impact the overall performance? Could the authors provide a more detailed analysis or comparison with other selection strategies?", "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": "KZ1hUA0414", "reviewer": "Reviewer_JJYk", "summary": "This article proposes a new metric, AgScore, for pseudo label filtering. It measures the accuracy of pseudo-labels by evaluating the similarity between a pixel's embedding and positive pixel embeddings, as well as the dissimilarity between it and negative pixel embeddings. This method can be integrated as a universal plugin into existing SSL frameworks and improves performance on various baseline models.", "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": "Thanks for taking the time to share your comments in the review assessment. We provide a detailed point-by-point response to your comments.\n\nNote that the following **link** refers to https://anonymous.4open.science/r/AgScore/Rebuttal.pdf\n\n---\n\n**Q1**: Advantages of AgScore.\n\n**A1**: Semi-supervised semantic segmentation is essentially a learning strategy-centric task, and its core lies in effectively filtering pseudo-labels to explore unlabeled data.\n\n- Previous **confidence-based methods tends not to be preferred ascribed to the trade-off** between TPR and FPR when handling pseudo-labels. A high confidence threshold ensures pseudo-label quality (low FPR) but discards many correct pseudo-labels with lower confidence (unfavorably low TPR), and vice versa.\n- We focus on the homogeneous pattern in the embedding space. Intuitively, **the high-dimensional, sophisticated embedding space has greater potential for assessing pseudo-label reliability than the relatively simple one-dimensional confidence metric from the prediction space**. This provides hints for assessing pseudo-label reliability in the embedding space.\n- In implementation, we absorb the merits of confidence to construct clean positive and negative agents. For pixel predictions difficult for confidence to handle, we employ the agent score function to score pseudo-labels in collaboration with clean positive/negative agents, **leveraging the embedding space's capability without sacrificing confidence's advantages, yielding higher TPR and lower FPR**.\n\n\nExperimentally: \n\n- Fig. 1 (d) shows that AgScore achieves a better TPR-FPR balance over confidence.\n- Fig. 3 depicts the better TPR and FPR dynamics of AgScore than confidence-based methods during training.\n\nThese experimental results, along with the superior segmentation performance (Tab. 1-4), provide strong evidence that AgScore reaches new heights in reliable pseudo label filtering beyond confidence.\n\n\nFurthermore, we attempt to analyze from the perspective of information theory. We prove that the mutual information between the pseudo labels selected by AgScore and the ground truth is greater than that of regular confidence-based methods (**Theorem G.3 in link**). Therefore, AgScore theoretically improves pseudo label selection compared to the confidence metric.\n\nIn summary, from the perspectives of **intuitive motivation**, **experimental analysis**, and **theoretical justification**, we demonstrate the advantages of AgScore over confidence-based methods. \n\n---\n\n**Q2**: About SAM.\n\n**A2**: We respectfully disagree with your viewpoint.\n\n- Semi-supervised learning focuses on better exploring vast amounts of unlabeled data under extremely limited labeled data.\n- In fact, even for powerful visual foundation models like SAM, they **struggle to generalize to domain-specific scenarios**, such as medical images, requiring fine-tuning to adapt to downstream tasks. In these tasks, obtaining sufficient annotations is challenging and time-consuming, making it highly valuable to explore how SAM can leverage easily accessible unlabeled data.\n\nTo further validate the value of SSL, we evaluate AgScore with SAM under the extreme 1-labeled case. As shown in **link** Tab. 8, we find: (1) The original SAM model struggles under the extreme 1-shot setting, underscoring the challenge of domain transfer for large vision models. (2) Fine-tuning SAM with a confidence-based pseudo-labeling strategy significantly boosts performance, demonstrating the value of SSL for adapting foundation models to specialized tasks. (3) Integrating AgScore into the SAM fine-tuning pipeline further improves results, validating the effectiveness of AgScore even with powerful vision backbones.\n\n---\n\n**Q3**: About RankMatch.\n\n**A3**: Thanks for pointing this out. RankMatch proposes a sample selection strategy via a confidence voting scheme in the embedding space to increase sample selection quantity for learning with noisy labels.\n\nHowever, we argue that a essential distinction is that **RankMatch focuses solely on positive samples** , neglecting the utilization of negative samples. Unlike RankMatch, **AgScore combines knowledge from both positive and negative samples** by examining the difference in embedding similarity between the candidate pseudo-label's corresponding pixel and the positive/negative agents to measure reliability, thus better leveraging the capabilities of the embedding space. Moreover, we provide a theoretical justification for AgScore's working mechanism.\n\nFurthermore, we construct an ablation study in **link** Tab. 9 to verify the impact of considering only positive agents (1-st entry), demonstrating that the introduction of negative agents improves performance.\n\n---\n\nWe hope our response can resolve your concern. Please do not hesitate to let us know if you have further questions :)\n\nThanks for your time and consideration. Have a wonderful day!\n\n[1] How to Efficiently Adapt Large Segmentation Model (SAM) to Medical Images.", "claims_and_evidence": "The empirical results and theory of this article reflect that AgScore is a better indicator for filtering pseudo labels.\nBut this paper fails to point out the reason why AgScore is superior to the vanilla confidence.", "methods_and_evaluation_criteria": "Yes I did.", "theoretical_claims": "Yes I did.", "experimental_designs_or_analyses": "Yes I did.", "relation_to_broader_scientific_literature": "The contribution of this paper lies in the more accurate indicator for filtering pseudo labels. However, there are two main issues.\n1. Why is the AgScore better than vanilla confidence? The theoretical proof only indicates the rationality of AgScore, but cannot prove how it is better than existing metrics.\n2. Due to the emergence of the Universal Visual Large Models (SAM series), by 2025, the SSL task will no longer be challenging and have practical value.", "essential_references_not_discussed": "None.", "other_strengths_and_weaknesses": "Advantage:\n1. promising performances\n2. clear written\n\ndisadvantage:\n1. Due to Universal Visual Large Model, this task is not as valuable as before.\n2. The reason why AgScore can defeat vanilla confidence is not clear.", "other_comments_or_suggestions": "None.", "questions_for_authors": "Though I can find some technological differences, the design of the AgScore has similarity to some existing works (e.g., RankMatch[1]) that leverage inter-pixel relationships. Would you like me to elaborate on the essential distinctions between them?\n\nFor other questions, please see the Strengths and Weaknesses part.\n\n[1] Zhang Z, Chen W, Fang C, et al. Rankmatch: Fostering confidence and consistency in learning with noisy labels[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2023: 1644-1654.", "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: This paper received mixed reviews initially. After rebuttal, three reviewers increase their scores, and now all reviewers lean to accept the paper. Two reviewers still have some concerns. One has a concern about the value of semi-supervised semantic segmentation in the era of large models, e.g., SAM; The other has a concern about the reliability of the proposed method. The AC recommends acceptance, but the authors are required to address these concerns in the camera-ready version.
Decision: Accept (poster)