Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation

Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-14 02:15:11

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.

Main paper: 
 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, isEQUATIONwhere S = ( S1 , . . . , SK ) 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 lossEQUATIONis based on Kullback-Leibler (KL) divergence. Figure 2 (b) shows some results for a generic segmentation network (ResNet101 [4] backbone) trained on PASCAL [5] using only image-level supervision with approximate size targets (8% mean relative errors). Our total loss is very simple: it combines size-target loss (2) and a common CRF loss (3) [6] . Size target (ours) shows per-image labeling times averaged over the data and mean Intersection-over-Union (mIoU) for comparable end-to-end methods with similar ResNet backbones (ResNet101 or WideResNet38 [1] ), for fairness. We obtained mIoU scores, except for the "tag" and "box" scores from [2] and [3] . Our supplemental materials detail evaluation of the labeling times and mIoU. For completeness, Tab.2 includes more complex architectures and multi-stage systems, e.g. for tags. This paper focuses on standard segmentation architectures for size supervision.36By weakly-supervised semantic segmentation we refer to all methods that do not use full pixelprecise ground truth (GT) masks for training. Such full supervision is overwhelmingly expensive for segmentation and is unrealistic for many practical purposes, see the right image in Fig. 1 . There are many forms of weak supervision for semantic segmentation, e.g. based on partial pixel-level ground truth defined by "seeds" [6, 7] , boxes [3] , or image-level class-tags [2, 8, 9] , see Fig. 1 . It is also common to incorporate self-supervision based on various augmentation ideas and contrastive losses [10] [11] [12] .Lack of supervision also motivates unsupervised loss functions such as standard old-school regularization objectives for low-level segmentation or clustering. For example, many methods [13, 14, 12] use variants of K-means objective (squared errors) enforcing the compactness of each class representation.It is also very common to use CRF-based pairwise loss functions [6, 7] that encourage segment shape regularity and alignment to intensity contrast edges in each image [15] . The last point addresses the well-known limitation of standard segmentation networks that often output low-resolution segments.Intensity contrast edges on the high-resolution input image is a good low-level cue of an object boundary and it can improve the details and localization of the semantic segments.Conditional or Markov random fields (CRF or MRF) are common basic examples of pairwise graphical models. The corresponding unsupervised loss functions can be formulated for continuous soft-max predictions S p produced by segmentation networks, e.g. [6, 7, 9] . Thus, it is natural to use relaxations of the standard discrete CRF/MRF models, such as Potts [16] or its dense-CRF version [17] . We use a bilinear relaxation of the general Potts modelEQUATIONwhere S := (S p | p ∈ Ω) is a field of all pixel-level soft-max predictions S p in a given image, andS k := (S k p | p ∈ Ω) is a vector of all pixel predictions specifically for class k. Matrix W = [w pq ]typically represents some given non-negative affinities w pq between pairs of pixels p, q ∈ Ω. It is easy to interpret loss (3) assuming, for simplicity, that all pixels have confident one-hot predictions S p so that each S k is a binary indicator vector for segment k. The loss sums all weights w pq between the pixels in different segments. Thus, the weights are interpreted as discontinuity penalties. The loss minimizes the discontinuity costs [16] .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 costs between neighboring pixels are often set by a Gaussian kernel w pq = exp-∥Ip-Iq∥ 2 2σ 2of given bandwidth σ, 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. Weakly supervised segmentation methods may also use partial pixel-level ground truth where only some subset Seeds ⊂ Ω of image pixels has class labels [6, 7, 9] . In this case it is common to use partial cross-entropy lossEQUATION)where y p is the ground truth label at a seed pixel p.Segmentation and classification methods often use "balancing" losses. In the context of classification, image-level predictions can be balanced over the whole training data. For segmentation problems, pixel-level predictions can be balanced within each training image. Our loss is an example of size balancing. Below we review some examples of related balancing loss functions used in prior work.Fully supervised classification. It is common to modify the standard cross-entropy loss to account for unbalanced training data where some classes are represented more than others. One common example is weighted cross-entropy, e.g. defined in [19] for image-level predictions S i asEQUATION)where class weights w k ∝ 1 1-β v k are motivated as a re-balancing factor based on the class distribution v in the training dataset D and β is a hyper-parameter. In the fully supervised setting, the purpose of re-weighting cross-entropy is not to make the predictions even closer to the known labels, but to decrease over-fitting to over-represented classes, which improves the model's generality.Unsupervised classification. In the context of clustering with soft-max models [20, 21] it is common to use fairness loss encouraging equal-size clusters. In this case, there is no ground truth and fairness is one of the discriminative properties enforced by the total loss in order to improve the model predictions on unlabeled training data. The fairness was motivated by information-theoretic arguments in [20] deriving it as a negative entropy of the data-set-level average predictionEQUATIONwhere u = ( 1 K , . . . , 1 K ) is a uniform categorical distribution, and symbol c = indicates that the equality is up to some additive constant independent of Ŝ. Perona et al. [21] pointed out the equivalent KLdivergence formulation of the fairness in (6) and generalized it to a balanced partitioning constraintEQUATIONwith any given prior distribution v that could be different from uniform.Semantic segmentation with image-level supervision. Most weakly-supervised semantic segmentation methods use losses based on segment sizes. This is particularly true for image-level supervision techniques [2, 9, 22, 23] . Clearly, segments for tag classes should have positive sizes, and segments for non-tag classes should have zero sizes.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) .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 asEQUATIONencouraging each non-tag class to have zero average prediction Sk , which implies zero predictions S k p at each pixel. Their expansion lossEQUATIONencourages positive average predictions Sk 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) impliesEQUATIONwhich 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 likeEQUATIONthat have flat bottoms to avoid unintended bias to some specific size target inside the probability simplex ∆ K . Similar thresholded barriers are common [22] .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] [25] [26] [27] [28] [29] [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. 
size-target loss and its properties: 
 Our proposed total loss is very simpleEQUATIONwhere 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 .EQUATIONAs 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 ExperimentsQuestion: Does the paper discuss the limitations of the work performed by the authors?Answer: [No] Justification: Although the limitations were not explicitly detailed in the paper, we mentioned that only a subset of the PASCAL dataset was labeled due to resource constraints, see Sec.To address this, we generated approximate synthetic size targets by corrupting the exact size targets. This allowed us to evaluate our method on the entire PASCAL dataset, as well as on COCO and ACDC datasets.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.• The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.• The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.• The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.• The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.• If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.• While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
experimental settings: 
 Datasets. We evaluate our approach on three segmentation datasets: PASCAL VOC 2012 [5] , MS COCO 2014 [31] , and 2017 ACDC Challenge1 [32] . The PASCAL dataset contains 21 classes. We adopt the augmented training set with 10,582 images [33] , following the common practice [34, 9] . 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 v = (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 asEQUATION)where vk 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) .When used, synthetic targets v = (v k ) K k=1 are generated by corrupting the exact targetsEQUATION) 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 fromEQUATION)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. We evaluate 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. 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.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.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). 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. (15, 16) . Our approach does not require any complex architectural modification or multi-stage training procedures needed for tag supervision, see "Modification" column. full supervision performance on the Pascal dataset. Despite its simplicity, the size-target approach outperforms other complex single-stage methods on both datasets.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 isEQUATION)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) = ( Sobj ) 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. 
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. 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. Guidelines: 
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Decision: This paper is a borderline case, with reviewers giving scores centered around 5 (more precisely: 4, 5, 5, 6). The reviewers appreciated the idea of using relatively cheap object size supervision in addition to the traditional image-level labels. However, they do not seem convinced that the moderate improvement in performance is large enough to justify the extra manual annotation effort (especially considering that modern image-level supervised methods already reach performance rather close to pixel-level supervised methods).

There is also a closely related work from 2016 proposing a similar idea (i.e. using object size estimates to help weakly supervised learning of object detectors, albeit at the bounding-box level rather than at the segment level): https://arxiv.org/abs/1608.04314. This reduces the novelty of the proposed approach.

When taking all factors into account, the Area Chair recommends rejection.

Reviews: review: paper_summary: paper_summary: The 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.
strength_weakness: strength_weakness: 1. The title of the paper is misleading. It claims that approximate size targets are sufficient, but the work also uses image labels for supervision.
2. 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.
3. 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.
4. In Table 1, the authors should also report the speed of tag annotation to highlight the cost of estimating target sizes.
5. 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.
questions: questions: See Q3 and Q5 in weaknesses, pls.
limitations: limitations: The authors do not discuss the limitations and broader impact of their method, which necessitates a dedicated discussion.
paper_summary: paper_summary: This 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.
strength_weakness: strength_weakness: 1. 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.

2. Lack of related work. The paper’s logical flow and organization need improvement. 

3. The paper lacks comprehensive comparisons with the latest models, such as "SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation (AAAI24)".
questions: questions: see the Weaknesses
limitations: limitations: 1. Fig and Figure are inconsistent in Line 24
paper_summary: paper_summary: The 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.
strength_weakness: strength_weakness: 1. 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.
2. 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.
questions: questions: Is it possible to give ablation experiments about these two losses? How much does the two losses affect the performance of the model?

For this method, is it possible to give more information about the time comparisons of the various methods in terms of time spent on labeling?
limitations: limitations: The 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.
paper_summary: paper_summary: This 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.
strength_weakness: strength_weakness: 1.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.

However, 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.

2.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.
questions: questions: Will the code, the labeling tool, and the labeled images be publicly available, so the community can use these tools to annotate their own datasets?
limitations: limitations: No negative societal impact.

Comments: title: extra comments on using more "complex" datasets
on more complex datasets, etc...
comment: We noticed that our response to reviewer thaL may be helpful to clarify our view on the need to test more complex datastes, which is similar to one of your points. However, we realized you might not see our response to that reviewer. Here is a copy of our relevant reply to thaL.

**[for us, complex are classes, not datasets]**:  A motivation for re-evaluating our ideas on more datasets is based on a speculation that human size annotation accuracy will decrease on more "complex datasets", whatever that may mean. Since we ask annotators to size only one class at a time, it makes sense to focus the discussion on some specific class that might be harder to size than classes on PASCAL. Could you please name some particular class on Cityscapes or DE20K that you think is harder to size than “birds” and why you think so? We do not see how image resolution or abstract “scene complexity” is relevant. If “annotation granularity” means high-frequency boundary details, they are mostly irrelevant for size estimation accuracy (due to “averaging”), unless many thin structures dominate the object shape, as often the case with birds.

We agree that harder-to-size classes may exist, but it would be helpful to have a specific example from the datasets you suggested that would make this discussion less speculative. We believe that significantly higher human errors are possible for some extreme examples of classes that this discussion can identify (we can name them in limitations). This may also degrade the corresponding results. However, our results are sufficiently convincing for many representative objects that could be found in practical applications. Things work even for sufficiently challenging classes like birds. One should also keep in mind that better assistance tools could be designed for specific complex classes. In general, our results are meant to be a proof-of-concept and we believe they sufficiently demonstrate the potential of our novel ideas.
Thanks for your feedback inspiring an interesting discussion. We would like to bounce back several related thoughts. 

**A [complex classes, not datasets]**: Your motivation for re-evaluating our ideas on more datasets is based on a speculation that human size annotation accuracy will decrease on more "complex datasets", whatever that may mean. Since we ask annotators to size only one class at a time, it makes sense to focus the discussion on some specific class that might be harder to size than classes on PASCAL. **Could you please name some particular class on Cityscapes or DE20K that you think is harder to size than “birds” and why you think so?** We do not see how image resolution or abstract “scene complexity” is relevant. If “annotation granularity” means high-frequency boundary details, they are mostly irrelevant for size estimation accuracy (due to “averaging”), unless many thin structures dominate the object shape, as often the case with birds.   

We agree that harder-to-size classes may exist, but it would be helpful to have a specific example from the datasets you suggested that would make this discussion less speculative. We believe that significantly higher human errors are possible for some **extreme** examples of classes that this discussion can identify (we can name them in limitations). This may also degrade the corresponding results. However, our results are sufficiently convincing for many representative objects that could be found in practical applications. Things work even for sufficiently challenging classes like birds. One should also keep in mind that **better assistance tools could be designed for specific complex classes**. In general, our results are meant to be a **proof-of-concept** and we believe they sufficiently demonstrate the potential of our novel ideas.

Do you seriously doubt that our novel ideas could be useful?

**B. [accuracy is an issue for all forms of supervision]**: For example, more complex objects lead to more mistakes for boxes, scribbles, and full pixel-masks in particualr. We saw many mistakes in the (so-called) ground truth masks in PASCAL (missing birds in a large group, ignored fine or thin shape details, e.g. in bikes, ambiguous categories like a "WV camper" car labeled as a bus, or toy labeled as a truck). That is, the accuracy of supervision is a general issue not limited to size annotations. Its full understanding is well beyond the scope of our work. in particular, likely problems with ground truth for complex classes (or datasets) may complicate the analysis of the effect of size annotation accuracy.  

**C. [room for future work]**: More labeled data and more experiments are always good, but we propose to leave some for future work (e.g. for a journal version), particularly because it would require a significant amount of annotation just to identify some specific “hard” classes. 

**E. [prior WSSS literature mostly use PASCAL. Maybe it is sufficient for concept proving?]** Note that the most significant/influential/conceptual prior work on WSSS are focused on PASCAL, to the best of our knowledge. While one can argue that other datasets are more complex (in some ways), it is more complex for all methods – full or weakly supervised.  In particular, are there examples in prior WSSS work where comparison on PASCAL is reversed on other datasets?
Thanks for the author's response. However, I think it's a consensus in the segmentation community that PASVAL VOC is much simpler than ADE20K and Cityscapes from various aspects, including the image resolution, the scene complexity, the annotation granularity. The segmentation performance for WSSS methods on these benchmarks also reveals this. Hence it is strongly recommended to experiment with these benchmarks, which can make this work more comprehensive.
> The response seems superficial and doesn't address my concerns effectively. 

Superficial? We tried our best and in good faith, but please consider that in some cases we had to guess what your real concerns are. We hope to gradually reduce misunderstandings.

Q1 is a good example. We had to guess the question presented in the **but** part:
> “It claims that approximate size targets are sufficient, **but** the work also uses image labels for supervision.”

We interpreted that as a concern that our paper generally hides the fact that size targets are an extension of tags. Our rebuttal addresses this in detail. If you are specifically concerned just about the title, please note that all other forms of weak supervision (boxes, points, etc) also implicitly include tag info, but no prior work explicitly states that in their titles. 

We also think that it is rather obvious that size targets “extend” class tags. In any case, the reader does not have to wait too long and this is emphasized right in the abstract. We like our title, but we are also open to your suggestions about it.

> In Q4: Figure 1 shows the average tag annotation, while **Table 1 should reflect the annotation speed for three different classes**. How are these related?

Table 1 has size target annotation speed for three classes individually only because our annotation assistance tool works with one class at a time and we can collect such info separately for each class. All timing info on other forms of weak supervision (in Figure 1) is collected from prior work [46]. We do not have any information about tagging individual classes. This information may not be even possible depending on how the corresponding assistance tools work.


> In Q3: The main question is **whether an 8% MRE is challenging for human annotation**.
 
As we hoped to clarify in the rebuttal, mRE is irrelevant here (it is mainly needed as a scalar tuning parameter controlling our noise model). We believe a more relevant question for our paper is **whether the quality of human size annotation is good enough to train the network to produce the segmentation quality comparable with full pixel-level supervision**. Our experiments conclusively confirm this is possible. The consistency of our results (human and synthetic, across three datasets) is sufficiently convincing, in our opinion.

First, we use (our) human annotation for a subset of PASCAL on cat, dog, and bird classes, individually (see binary segmentation in Table 1) and together (four-class segmentation in Figure 5 right). For example, the latter achieves 89.6% mIOU for all 4 classes, while full supervision is 92.2% for the same four classes. "Bird" is a particularly complex class on PASCAL due to the huge variation in size and plurality of objects. For birds only, human annotation achieves 86.4% accuracy, while full supervision is 88.8%. (Table 1) We are not sure why the reviewer thinks that our classes are not representative of PASCAL 

Second, due to limited human resources, we could not size-annotate all PASCAL classes, COCO, or other segmentation datasets. Instead, we developed a synthetic noise model for corrupting true sizes easily available on all these sets. Our noise model was tuned to match the segmentation quality for human annotation on available three PASCAL classes (which vary in complexity). Assuming that our tuning by matching segmentation quality is technically justified (see next point), we provide further evaluations on all PASCAL classes and COCO, which further confirm our claim. Of course, we can not guarantee that our ideas work in 100% of all cases. No one can in computer vision. We could not claim that even if we fully human-annotated PASCAL, COCO, Cityscapes, ADE20K, etc. However, we believe we provided sufficient evidence on real and synthetic experiments for the promising potential of our ideas.

> **Other classes might influence** the predictions for these three (cat, dog, bird).

The following answer is based on a particular guess of the real concern here. When matching the segmentation quality (for synthetic and human annotations) we use the same setting for both (e.g. four-class segmentation). So “other classes” have no influence here. When adding “more classes”, they should make the prediction problem equally more challenging for both human and synthetic annotations. However, “more classes” do not affect the size annotation accuracy in both cases. Indeed, we corrupt target sizes independently for each class, the humans also evaluate only one class at a time. In both cases, we convert such corrupted sizes to a distribution by normalization mainly for the sake of the loss function (KL divergence), but this is the same process for human or synthetic targets.

If this does not help, please elaborate. We will try again.

> prove that images in Pascal are **diverse enough** or **more diverse**

We can not do that. The claim that it is not diverse enough is also speculative. Pls. see the next reply
The response seems superficial and doesn't address my concerns effectively. Here are the specific issues:

In Q1: My issue is that the title is misleading, but the authors primarily discuss the abstract, which doesn't resolve the problem.

In Q4: Figure 1 shows the average tag annotation, while Table 1 should reflect the annotation speed for three different classes. How are these related?

In Q3: The main question is whether an 8% MRE is challenging for human annotation. It is good for the authors to conduct experiments on three classes, but these classes don't represent all classes in the VOC dataset. Additionally, the mIOU for these three classes differs from the mIOU for the entire dataset, as other classes might influence the predictions for these three. Therefore, using segmentation accuracy for only three classes (Figure 5) might not accurately reflect matching of mREs of human annotation and synthetic data.

In Q3: The authors claim that PASCAL is highly diverse, but is there any evidence to support this? COCO is treated as an example, but what about datasets with many images containing multiple instances of the same semantic class, as shown in Figure 1?