Title: Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation
Venue: NeurIPS 2024
Accepted: no
Keywords: Cardinality of segments

KL divergence
Abstract: We propose a new general form of image-level supervision for semantic segmentation based on approximate targets for the relative size of segments. At each training image, such targets are represented by a categorical distribution for the "expected" average prediction over the image pixels. We motivate the zero-avoiding variant of KL divergence as a general training loss for any segmentation architecture leading to quality on par with the full pixel-level supervision. However, our image-level supervision is significantly less expensive, it needs to know only an approximate fraction of an image occupied by each class. Such estimates are easy for a human annotator compared to pixel-accurate labeling. Our loss shows significant robustness to size target errors, which may even improve the generalization quality. The proposed size targets can be seen as an extension of the standard class tags, which correspond to non-zero size targets in each image. Using only a minimal amount of extra information, our supervision improves and simplifies the training. It works on standard segmentation architectures as is, unlike tag-based methods requiring complex specialized modifications and multi-stage training.

Abstract

We propose a new general form of image-level supervision for semantic segmentation based on approximate targets for the relative size of segments. At each training image, such targets are represented by a categorical distribution for the 'expected' average prediction over the image pixels. We motivate the zero-avoiding variant of KL divergence as a general training loss for any segmentation architecture leading to quality on par with the full pixel-level supervision. However, our image-level supervision is significantly less expensive, it needs to know only an approximate fraction of an image occupied by each class. Such estimates are easy for a human annotator compared to pixel-accurate labeling. Our loss shows significant robustness to size target errors, which may even improve the generalization quality. The proposed size targets can be seen as an extension of the standard class tags, which correspond to non-zero size targets in each image. Using only a minimal amount of extra information, our supervision improves and simplifies the training. It works on standard segmentation architectures as is, unlike tag-based methods requiring complex specialized modifications and multi-stage training.

1 Introduction

Our image-level supervision approach applies to any semantic segmentation model and does not require any modification. It can be technically described in one paragraph, as follows. Soft-max prediction S p = ( S 1 p , . . . , S K p ) at any pixel p is a categorical distribution over K classes, including background. At any image, the average prediction over all image pixels, denoted by set Ω , is

where ¯ S = ( ¯ S 1 , . . . , ¯ S K ) is also a categorical distribution over K classes. It is an image-level prediction of the relative or normalized sizes (volume, area, or cardinality) of the objects in the image. We assume that training images have approximate size targets represented by categorical distributions v = ( v k ) K k =1 , e.g. v = (0 , . 15 , 0 , . . . , 0 , . 75) for the middle image in Fig. 1 if 'bird' is the second class and 'background' is the last. This representation also applies to multi-label images. For each training image, our size-target loss

is based on Kullback-Leibler ( KL ) divergence. Figure 2(b) shows some results for a generic 27 segmentation network (ResNet101 [4] backbone) trained on PASCAL [5] using only image-level 28 supervision with approximate size targets ( 8% mean relative errors). Our total loss is very simple: it 29 combines size-target loss (2) and a common CRF loss (3) [6]. 30

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1.1 Overview of weakly-supervised segmentation

By weakly-supervised semantic segmentation we refer to all methods that do not use full pixel32 precise ground truth (GT) masks for training. Such full supervision is overwhelmingly expensive for 33 segmentation and is unrealistic for many practical purposes, see the right image in Fig. 1. There are 34 many forms of weak supervision for semantic segmentation, e.g. based on partial pixel-level ground 35 truth defined by 'seeds' [6, 7], boxes [3], or image-level class-tags [2, 8, 9], see Fig. 1. It is also 36 common to incorporate self-supervision based on various augmentation ideas and contrastive losses 37 [10-12]. 38

Lack of supervision also motivates unsupervised loss functions such as standard old-school regulariza39 tion objectives for low-level segmentation or clustering. For example, many methods [13, 14, 12] use 40 variants of K-means objective (squared errors) enforcing the compactness of each class representation. 41 It is also very common to use CRF-based pairwise loss functions [6, 7] that encourage segment shape 42 regularity and alignment to intensity contrast edges in each image [15]. The last point addresses the 43 well-known limitation of standard segmentation networks that often output low-resolution segments. 44 Intensity contrast edges on the high-resolution input image is a good low-level cue of an object 45 boundary and it can improve the details and localization of the semantic segments. 46

Conditional or Markov random fields (CRF or MRF) are common basic examples of pairwise 47 graphical models. The corresponding unsupervised loss functions can be formulated for continuous 48 soft-max predictions S p produced by segmentation networks, e.g. [6, 7, 9]. Thus, it is natural to use 49 relaxations of the standard discrete CRF/MRF models, such as Potts [16] or its dense-CRF version 50 [17]. We use a bilinear relaxation of the general Potts model 51

where S := ( S p | p ∈ Ω) is a field of all pixel-level soft-max predictions S p in a given image, and 52 S k := ( S k p | p ∈ Ω) is a vector of all pixel predictions specifically for class k . Matrix W = [ w pq ] 53 typically represents some given non-negative affinities w pq between pairs of pixels p, q ∈ Ω . It is 54 easy to interpret loss (3) assuming, for simplicity, that all pixels have confident one-hot predictions 55 S p so that each S k is a binary indicator vector for segment k . The loss sums all weights w pq between 56 the pixels in different segments. Thus, the weights are interpreted as discontinuity penalties. The loss 57 minimizes the discontinuity costs [16]. 58

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costs between neighboring pixels are often set by a Gaussian kernel bandwidth

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w

pq

= exp

In practice, affinity weights w pq are set close to 1 if two neighboring pixels p, q have similar intensities, and weight w pq is set close to zero either when two pixels are far from each other on the pixel grid or if they have largely different intensities [6, 16, 17]. The affinity matrix W could be arbitrarily dense or sparse, e.g. many zeros when representing a 4-connected pixel grid. The non-zero discontinuity

-∥

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of given

, which works as a soft threshold for detecting high-contrast intensity edges in the image.

Thus, loss (3) encourages both the alignment of the segmentation boundary to contrast edges in the

(high-resolution) input image and the shortness/regularity of this boundary. 66

q

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(b)

(a)

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Weakly supervised segmentation methods may also use partial pixel-level ground truth where only 67 some subset Seeds ⊂ Ω of image pixels has class labels [6, 7, 9]. In this case it is common to use 68

partial cross-entropy loss 69

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where y p is the ground truth label at a seed pixel p .

1.2 Related balancing losses

Segmentation and classification methods often use 'balancing' losses. In the context of classification, 72 image-level predictions can be balanced over the whole training data. For segmentation problems, 73 pixel-level predictions can be balanced within each training image. Our loss is an example of size 74 balancing. Below we review some examples of related balancing loss functions used in prior work. 75

Fully supervised classification. It is common to modify the standard cross-entropy loss to account 76 for unbalanced training data where some classes are represented more than others. One common 77 example is weighted cross-entropy , e.g. defined in [19] for image-level predictions S i as 78

where class weights w k ∝ 1 1 -β v k are motivated as a re-balancing factor based on the class distribution 79 v in the training dataset D and β is a hyper-parameter. In the fully supervised setting, the purpose 80 of re-weighting cross-entropy is not to make the predictions even closer to the known labels, but to 81 decrease over-fitting to over-represented classes, which improves the model's generality. 82

Unsupervised classification. In the context of clustering with soft-max models [20, 21] it is common 83 to use fairness loss encouraging equal-size clusters. In this case, there is no ground truth and 84 fairness is one of the discriminative properties enforced by the total loss in order to improve the model 85 predictions on unlabeled training data. The fairness was motivated by information-theoretic arguments 86 in [20] deriving it as a negative entropy of the data-set-level average prediction ˆ S := 1 | D | ∑ i ∈ D S i 87 for dataset D 88

where u = ( 1 K , . . . , 1 K ) is a uniform categorical distribution, and symbol c = indicates that the equality 89 is up to some additive constant independent of ˆ S . Perona et al. [21] pointed out the equivalent KL90 divergence formulation of the fairness in (6) and generalized it to a balanced partitioning constraint 91

with any given prior distribution v that could be different from uniform. 92

Semantic segmentation with image-level supervision. Most weakly-supervised semantic segmenta93 tion methods use losses based on segment sizes. This is particularly true for image-level supervision 94 techniques [2, 9, 22, 23]. Clearly, segments for tag classes should have positive sizes, and segments 95 for non-tag classes should have zero sizes. 96

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Similarly to our paper, size-based constraints are often defined for the image-level average prediction ¯ S , see (1), computed from pixel-level predictions S p . Many generalized forms of pixel-prediction averaging can be found in the literature, where they are often referred to as prediction pooling . Some decay parameter often provides a wide spectrum of options from basic averaging to max-pooling. While the specific form of pooling matters, for simplicity, we discuss the corresponding balancing loss functions assuming basic average prediction ¯ S in (1).

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One of the earliest works on tag-supervised segmentation [9] uses log-barriers to 'expand' tag objects in each training image and to 'suppress' the non-tag objects. Assuming image tags T , their suppression loss is defined as

encouraging each non-tag class to have zero average prediction ¯ S k , which implies zero predictions S k p at each pixel. Their expansion loss

encourages positive average predictions ¯ S k and non-trivial tag class segments.

We observe that the expansion loss (9) may have a bias to equal-size segments, as particularly evident in the case of average predictions. Indeed, (9) implies

which is a special case of our size loss (2) when the size target v = u T is a uniform distribution over tag classes. The intention of the log barrier loss (9) is to push image-level size prediction ¯ S from the boundaries of the probability simplex ∆ K corresponding to the zero-level for the tag classes T . Figure 2(a) shows the results for training based on the total loss combining CRF loss (3) with the log-barrier loss (9). Its unintended bias to equal-size segments (10) is obvious. Note that the mentioned decay parameter used for generalized average predictions should reduce such bias.

Alternatively, it may be safer to use barriers for ¯ S like

that have flat bottoms to avoid unintended bias to some specific size target inside the probability simplex ∆ K . Similar thresholded barriers are common [22].

1.3 Contributions

In general, it would be great to have effective image-level supervision for segmentation that only uses barriers like (9) or (11) since they do not require any specific size targets. This corresponds to tag-only supervision. However, our empirical results for semantic segmentation using such barriers were poor and comparable with those in [9]. A number of more recent semantic segmentation methods for tag-level supervision have considerably improved such results [12, 24-30], but they introduce significantly more complex multi-stage training procedures and various architectural modifications, which makes such methods hard to replicate, generalize, or to understand the results. We are focused on general easy-to-understand end-to-end training methods. Our main contributions are:

We propose and evaluate a new general form of weak supervision, size targets. The sizetarget supervision can be approximate and is relatively easy to get from human annotators.

We propose the zero-avoiding variant of KL divergence as a general training loss, allowing our end-to-end size-target approach to be integrated with any segmentation architecture.

Comprehensive experiments with our size-target method demonstrate state-of-the-art performance across multiple datasets using standard segmentation models typically employed for full supervision, without any architectural modifications.

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2 Size-target loss and its properties

Our proposed total loss is very simple

where the two terms are our size-target loss (2) and standard CRF loss (3). The core new component is our size-target loss based on the forward KL-divergence. Our size-target loss (2) encourages specific target volumes for tag classes. Additionally, the size-target loss suppresses non-tag classes, encouraging zero volumes for classes not in the image. The CRF loss also contributes to the suppression of redundant classes. Therefore, unlike most prior work on imagelevel supervision for semantic segmentation, e.g. [9, 2, 12], we do not need separate suppression loss terms like (8). We validated this claim experimentally, they did not change the results.

The size-target loss can also be integrated into other weakly-supervised settings, e.g. partial cross-entropy loss (4) commonly used for seeds. We show that using approximate size targets can significantly improve the seed-supervised segmentation in [6] when the seed lengths are short, see the right plot of Fig. 4.

As is well known, KL divergence is asymmetric. In our work on image-level supervised segmentation, the order of the estimated and target distributions is crucial. The forward KL divergence possesses a zero-avoiding property, as illustrated in Fig. 3. Specifically, forward KL divergence imposes an infinite penalty when any class with a non-zero target is predicted as zero. In contrast, the penalty of the reverse KL divergence is finite and much weaker. When using reverse KL divergence, segmentation models tend to generate trivial solutions, predicting all pixels as the background class. This issue likely arises due to dataset imbalance, where the background class is prevalent. The zero-avoiding property of forward KL divergence ensures that segmentation models do not produce trivial solutions and predict all classes in the image tag sets.

3 Experiments

3.1 Experimental settings

Datasets. We evaluate our approach on three segmentation datasets: PASCAL VOC 2012 [5], MS COCO 2014 [31], and 2017 ACDC Challenge 1 [32]. The PASCAL dataset contains 21 classes. We adopt the augmented training set with 10,582 images [33], following the common practice [34, 9]. Validation and testing contain 1449 and 1456 images. Seed supervision of the PASCAL dataset is from [7]. COCO has 81 classes with 80K training and 40K validation images. ACDC Challenge is to segment the left ventricular endocardium. The training and validation sets contain 1674 and 228 images. The exact size targets are extracted from the ground truth masks.

Approximate size targets. We train segmentation models using approximate size targets v = ( v k ) K k =1 generated for each image either by human annotators or by corrupting the exact size targets ˆ v = (ˆ v k ) K k =1 with different levels of noise. In all cases, we report the segmentation accuracy on validation data together with mean relative error (mRE) of the corresponding corrupted size targets. For each training image containing class k , the relative error for the size target v k is defined as

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where ˆ v k is the exact size. mRE averages RE over all images and all classes. For human annotated size targets v = ( v k ) K k =1 , the relative size errors are computed directly from the definition (14).

where ϵ is white noise with standard deviation σ controlling the level of corruption and operator ←-represents re-normalization ensuring corrupted targets ( v k ) K k =1 add up to one. Equation (15) defines random variable v k as a function of ϵ . Thus, in this case, mRE can be analytically estimated from σ

where E is the expectation operator. The approximation in the middle uses (15) as an equality ignoring re-normalization of the corrupted sizes, and the last equality is a closed-form expression for the mean absolute deviation (MAD) of the Normal distribution N (0 , σ ) .

Evaluation metrics for segmentation. We employ mean Intersection-over-Union (mIoU) as the evaluation criteria for PASCAL and COCO, and mean Dice similarity coefficient (DSC) for the ACDC dataset. The quality on the PASCAL test set is assessed on the online evaluation server.

Implementation details. Weevaluate our approach with two types of ResNet-based [4] and one vision transformer (ViT) based [35] segmentation models on the PASCAL and COCO datasets. ResNetbased models follow the implementation of DeepLabV3+ [18] using the backbone of ResNet101 (R101) or the backbone of WideResNet-38 (WR38) [1]. For brevity, we name them R101-based or WR38-based DeepLabV3+ models. For the ViT-based network, We follow the implementation of Segmenter [36], adopting its ViT-B/16 backbone and linear decoder. For experiments on the ACDC datasets, we use MobileNetV2-based [37] DeepLabv3+ model. The R101, WR38, and MobileNetV2 backbones are ImageNet [38] pre-trained. ViT-B/16 backbone is pre-trained on ImageNet-21K [39] and fine-tuned on ImageNet-1k [38]. We directly evaluate our size-target approach on top of the standard architectures without any modification.

Images are resized to 512 × 512 for PASCAL and COCO, and 256 × 256 for ACDC. We employ color jittering and horizontal flipping for data augmentation. Segmentation models are trained with stochastic gradient descent on one RTX A6000 GPU with 48 GB GDDR6: 60 epochs for PASCAL and COCO, and 200 epochs for ACDC, with a polynomial learning rate scheduler (power of 0.9). Batch sizes are set to 16 for ResNet and 20 for ViT models on PASCAL, 12 on ACDC, and 12 (ResNet) and 16 (ViT) for MS COCO. The initial learning rate is 0.005 for ACDC and PASCAL's ResNet models, and 0.0005 for PASCAL's ViT models. The initial learning rate on COCO is 0.0005 for ResNet and 0.0001 for ViT models. Loss function (12) is employed for size-target supervision. Loss (13) is only used for seed supervision in Sec. 3.3. The implementation of CRF loss (3) is the same as [6]. We use 2e -9 as the weight of the CRF term following the strategy in [6]. Size-target loss (2) and pCE (4) are used for medical images.

3.2 Robustness to Size Errors

We show the size targets can be approximate. The left plot in Fig. 4 illustrates the robustness of our approach to size errors. Segmentation models are trained with synthetic size targets subjected to varying levels of corruption, as defined in (15). The validation accuracy (solid red line) only drops slightly when mRE (16) remains below 16%. The CRF loss (3) further enhances the robustness (solid blue line). When the relative error ( mRE ) is 4%, there is a noticeable increase in validation accuracy. The downward trend of the training accuracy (dashed blue line) suggests that the observed increases in validation accuracy at mRE = 4% stem from improved neural network generalization.

3.3 Enhancing seed-based segmentation with size targets

Our size-target approach can be integrated with partial ground truth mask supervision (seeds). The right plot in Fig. 4 demonstrates the results of seed-supervised semantic segmentation with and without size-target supervision. Size targets significantly enhance performance, especially when the seed lengths are short. Without size targets, segmentation performance degrades dramatically as the seed length decreases. Notably, when only one pixel is labeled for each object (seed length ratio = 0 . 0 ), size-target supervision boosts accuracy from 66.6% to 74%, approaching the performance of full seed supervision (seed length ratio = 1.0).

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3.4 Human-annotated size targets

Annotation tool. In this section, our approach is evaluated with size targets annotated by humans. We annotated training images for a subset of PASCAL classes, including cat, dog, and bird. A user interface with an assistance tool was developed to facilitate the annotation. The assistance tool overlays grid lines partitioning the image into 5 × 4 small rectangles or 3 × 3 large rectangles. Users can determine the size of a class in an image by counting rectangles (fractions allowed) or entering the percentage relative to the image size. Annotators can choose finer or coarser partitioning for each image depending on the object size. We evaluate relative errors with (14) for human annotations. Empirical evidence shows that annotators are approximately two times more accurate with the assistance tool, especially for small objects in the image. The last two columns of Table 1 report the annotation speed per image and mean relative error (14) for each class. The left plot in Fig. 5 shows the histograms of relative errors for human annotations. The histograms illustrate that annotated size errors are mostly below 10% , but occasional large mistakes (heavy tails) raise the mean error.

Segmentation with human-annotated size. Segmentation models trained with human-annotated size targets show robustness to human 'heavy tail' errors. We compare the accuracy for humanannotated and synthetic size targets in the right plot of Fig. 5. The accuracy for human-annotated size (indicated by the red star in the plot) approaches 97.2% (89.6%/92.2%) of the full supervision performance, demonstrating that size-target approach is significantly robust to human errors. Binary segmentation accuracy for each class is reported in the shaded cells in Table 1. The performance of

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binary segmentation models trained with human-annotated size targets is comparable to those trained with precise size targets.

3.5 Comparison with the state-of-the-art methods

Our general training losses are applied to three standard architectures (R101-DeepLabV3+, WR38DeepLabV3+, and ViT-Linear) for semantic segmentation as is, without any modification. Our results are highlighted in Table 2. The models are trained using synthetic size targets with an approximate mean relative error (mRE) of 8%. We chose this corruption level because its performance is close to human annotations, as shown in the right plot of Figure 5. Since our single-stage (end-to-end) approach is completely general, it is possible to use it in specialized architectures or complex training procedures. Likely, this would further improve the results, but this is not the focus of our work. The rest of Table 2 shows the results for semantic segmentation methods (of different complexities) for weak and full supervision. Methods are divided into multi-stage and single-stage methods, grouped by their backbones. Typical single-stage methods improve their results using complex architectural or training modifications such as additional training branches, extra refinement modules, or specialized training strategies. However, we achieve state-of-the-art using only standard segmentation architectures, commonly used in full supervision. The R101-based DeepLabV3+ model trained with approximate size targets approaches 92% (71.9/78.2) of its full supervision performance on PASCAL. The WR38-based DeepLabV3+ model trained with approximate size-target supervision surpasses other methods employing the same backbone by approximately 10%. Using the standard vision transformer architecture [36], the size-target approach achieves approximately 96% of the

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full supervision performance on the Pascal dataset. Despite its simplicity, the size-target approach outperforms other complex single-stage methods on both datasets.

3.6 Medical data: size-target vs. size-barrier

Our method is also promising for medical image segmentation, benefiting from the consistency in object sizes across similar medical images, which healthcare professionals can easily estimate. We compare our size-target approach with the thresholded size-barrier technique [22], proposed for the weakly supervised medical image semantic segmentation. The size-barrier loss enforces inequality size constraints. Given the lower bound of each class, the thresholded size-barrier loss is

where a k is a lower bound of class k . We train binary segmentation models with a combination of partial cross-entropy loss (4) and size constraint loss: size-target (2) or size-barrier (17). Seeds used in the experiments are obtained using the same method provided in [22]. The object and background barrier, a obj and a bg are set based on [22]. In the size-barrier experiments, similarly to [22], we suppress the non-tag classes, using the loss L sup ( S ) = ( ¯ S obj ) 2 . Conversely, size-target loss automatically suppresses non-tag classes as discussed in Sec. 2. The left plot in Fig. 6 displays the segmentation accuracy against different levels of size target corruption. Our size-target loss consistently outperforms size-barrier loss, maintaining its superiority even when using highly noisy size targets. The peak in the accuracy curve aligns with the experimental results in Sec. 3.2 and Sec. 3.4. The accuracy of the model trained using size targets with relative errors of 8% surpasses the full supervision performance. Additionally, using a fixed average size target across all training images can yield performance comparable to the size-barrier method, see the dashed red line in the left plot of Fig. 6. The right image in Fig. 6 shows qualitative examples of both methods.

4 Conclusions

We proposed a new image-level supervision for semantic segmentation: size targets. Such targets could be approximate. In fact, our results suggest that some errors can benefit generalization. The size annotation by humans requires little extra effort compared to the standard image-level tags and it is much cheaper than the full pixel-accurate ground truth masks. We proposed an effective size-target loss based on forward KL divergence between the soft size targets and the average prediction. In combination with the standard CRF-based regularization loss, our approximate size-target supervision on standard segmentation architectures (DeepLab and ViT) achieves state-of-the-art performance. Our general easy-to-understand approach outperforms significantly more complex weakly-supervised techniques based on model modifications and multi-stage training procedures.

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A Appendix / supplemental material

A.1 Labeling costs and accuracies reported in Figure 1

Labelling costs. Figure 1 in the paper shows labeling speed and accuracy for different forms of supervision on PASCAL VOC. The table at the bottom of Figure 1 shows ballpark estimates of average labeling time per image in the whole dataset. We use the data in [46], as well as Table 1 in the paper, and aggregate all labeling speeds from 'per class', 'per instance', or 'per point' to 'per image' using the average number of instances or classes in each image and the aggregation rules formulated in [46], see their Section 4. The top-left corner in each picture shows the corresponding estimated labeling times for the representative multi-instance image. All the labeling times are only rough estimates, but they are intuitive. The relative costs for point supervision seem underestimated, but they follow evaluation conventions detailed in [46].

Accuracies. The values of 'point', 'size target' and 'full supervision' accuracy (mIOU%) are based on the experiments in the paper (Figure 4). We follow the learning rate scheme in DeepLabV3+ [18] for the training with full supervision. For fairness, we compare these with end-to-end methods using similar ResNet backbones in tag[2] and box -supervision [3]. Typical SOTA methods for tag and box supervision use special architectural modifications, unlike our generic size-target loss, cannot be seamlessly plugged into any segmentation model.

A.2 Qualitative results

Figure 7 presents the qualitative examples of our method on PASCAL (left) and COCO (right) 458 validation sets. Despite size targets providing only image-level information, segmentation models 459 can precisely identify object locations, eliminating the need for localization methods like CAM. 460

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new weakly supervised semantic segmentation task. This task uses pixel-level categorical distribution as the label in the training stage. KL divergence is used as the training loss. Experiments on three public segmentation datasets show the effectiveness of the proposed method.\n\nSTRENGTHS:\n1.The proposed task is interesting. It provides the community another choice for segmentation with less annotation effort.\n\n2.The proposed KL divergence loss is effective, demonstrated by experiments on three public datasets. It achieves performance comparable to methods using more expensive labels, like the box supervised one.\n\n3.The proposed method is robust to size target error, which makes it more practical.\n\n4.The writing is fluent and easy to follow.\n\nWEAKNESSES:\n1.Labeling effort on complex images. Images from PASCAL VOC (like Figure 1) are easy to annotate. It contains few classes and the background is generally clean. The density of target objects is low, and hence it’s also suitable for the proposed grid-based size target annotation way.\n\nHowever, in practice, scenes are much more complex, with more classes, more crowded objects, and complex backgrounds. The authors are recommended to show the annotation effort on those images, like images from Cityscapes and ADE20K. I think when the scenes become more complex, the labeling effort will increase significantly. The labeling effort of size target will be much more than the tag way, since tagging will be less influenced in such cases.\n\n2.Model performance on complex images. Similarly, it’s recommended to evaluate the model’s performance with the proposed loss on these complex datasets. This will give a more comprehensive understanding of the proposed method.\n\nQUESTIONS:\nWill the code, the labeling tool, and the labeled images be publicly available, so the community can use these tools to annotate their own datasets?\n\nLIMITATIONS:\nNo negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper titled \"Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation\" proposes a novel method of semantic segmentation that leverages approximate size targets instead of full pixel-level supervision. The method involves using categorical distributions to represent the expected average prediction over image pixels, utilizing the zero-avoiding variant of KL divergence as a training loss. The approach aims to reduce annotation costs while maintaining segmentation accuracy comparable to full supervision.\n\nSTRENGTHS:\n1. Originality: The use of approximate size targets as a form of weak supervision for semantic segmentation is novel and creative.\n2. Quality: The experimental results are comprehensive and demonstrate the effectiveness of the proposed method across different datasets and segmentation architectures.\n3. Significance: The approach has significant implications for reducing annotation costs in semantic segmentation tasks, making it highly relevant to practical applications.\n\nWEAKNESSES:\n1. Simplicity of Method：While the proposed method is innovative, it seems relatively simple. There might be opportunities to enhance its contributions with further development or by integrating additional techniques.\n2. Limited Scope of Evaluation: While the paper evaluates the method on several datasets, it would benefit from a broader range of scenarios, including more diverse and complex images.\n\nQUESTIONS:\nIs it possible to give ablation experiments about these two losses? How much does the two losses affect the performance of the model?\n\nFor this method, is it possible to give more information about the time comparisons of the various methods in terms of time spent on labeling?\n\nLIMITATIONS:\nThe authors have addressed the limitations related to annotation errors and have demonstrated the robustness of their method to these errors. However, it would be beneficial to discuss potential limitations in more detail, such as the scalability of the method to larger and more diverse datasets, and any assumptions made about the nature of the size target annotations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel image-level supervision method for semantic segmentation using approximate segment size targets. It utilizes categorical distributions for expected average predictions, reducing annotation cost and complexity. The authors propose a zero-avoiding KL divergence as a training loss, compatible with any segmentation architecture, and demonstrate significant robustness to size target errors, improving generalization. The method achieves state-of-the-art performance on multiple datasets with standard segmentation models like ResNet101. Additionally, it requires minimal extra information and no architectural changes, making it a practical and effective solution for weakly-supervised semantic segmentation in real-world applications.\n\nSTRENGTHS:\n1. The paper introduces a novel form of image-level supervision for semantic segmentation using approximate segment size targets. This approach is original in its use of categorical distributions for expected average predictions, providing a fresh perspective on weakly-supervised segmentation methods.\n\n2. The quality of the research is high, with comprehensive experiments conducted on multiple datasets. The use of a zero-avoiding variant of KL divergence as a training loss is well-justified and demonstrates robustness to size target errors. The empirical results show that the method achieves state-of-the-art performance using standard segmentation models.\n\nWEAKNESSES:\n1. The paper claims robustness to size target errors but provides limited detailed analysis on this aspect. Including more experiments to quantify and analyze how different levels of size target errors impact performance would provide a clearer understanding of the method's robustness.\n\n2. Lack of related work. The paper’s logical flow and organization need improvement. \n\n3. The paper lacks comprehensive comparisons with the latest models, such as \"SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation (AAAI24)\".\n\nQUESTIONS:\nsee the Weaknesses\n\nLIMITATIONS:\n1. Fig and Figure are inconsistent in Line 24", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a novel image-level supervision method for semantic segmentation, utilizing approximate targets for the relative sizes of segments in training images. These targets, represented as categorical distributions for the expected average prediction over pixels, are integrated using a zero-avoiding variant of KL divergence as the training loss. This approach achieves quality comparable to full pixel-level supervision but is significantly less costly, requiring only rough estimates of the areas occupied by each class.\n\nSTRENGTHS:\n1. Using object size as a form of supervision is both innovative and interesting.\n2. The proposed method is straightforward and easy to understand.\n\nWEAKNESSES:\n1. The title of the paper is misleading. It claims that approximate size targets are sufficient, but the work also uses image labels for supervision.\n2. The most important comparison in Figure 1 is between 'Tag' and 'Size target,' as this validates the significance of using target size supervision. To clearly demonstrate that 'Size target' is superior to 'Tag' under identical conditions, it would be better to use the same architecture for both comparisons.\n3. Labeling the size of objects can be challenging for humans and may introduce significant noise, especially for tiny objects. Although the authors demonstrate impressive accuracy with up to 8% size target errors, this remains a stringent annotation standard, particularly for small objects. For instance, as seen in Table 1, the mean relative error (mRE) often exceeds 10% during human annotation in the Pascal VOC dataset. Moreover, estimating target sizes in Pascal VOC is relatively easy since objects are typically large and centered. However, labeling images in more complex datasets, such as COCO, might result in a higher mRE.\n4. In Table 1, the authors should also report the speed of tag annotation to highlight the cost of estimating target sizes.\n5. The proposed method is straightforward and impressive for its end-to-end training, especially considering that existing weakly supervised semantic segmentation (WSSS) methods typically use CAM and two-step training. However, as shown in Table 2, while the proposed method achieves comparable accuracy to state-of-the-art WSSS methods, it relies on additional supervision and a high annotation standard (8% mRE). Moreover, Table 2 indicates that the accuracy with only tag supervision is close to that of fully supervised methods, suggesting that tag supervision alone may be sufficient for segmentation.\n\nQUESTIONS:\nSee Q3 and Q5 in weaknesses, pls.\n\nLIMITATIONS:\nThe authors do not discuss the limitations and broader impact of their method, which necessitates a dedicated discussion.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-14"}