Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: Different from most existing methods, this paper investigates the FSS problem from the perspective of feature extraction. The authors claim that the heterogeneity of the sample-level, region-level and patch-level of support-query pair is the main obstacle to alleviate the intra-class variation. Thus, this paper proposes 3 novel modules (i.e. cross-sample attention, cross-region interaction and masked image segmentation) to tackle the above 3 problems, respectively. The proposed method sets new states of the art on many benchmarks.


strength_and_weaknesses: Strength:
(1) Investigation of the FSS problem from the perspective of feature extraction, which is interesting and is ignored in previous research.
(2) Summarizing the heterogeneity between support-query pair and within query image itself from 3 aspects: sample-level, region-level and patch level is a good idea.
(3) The idea of leverage cross-sample attention, cross-region interaction and masked image segmentation to suppress the intra-class variation is reasonable and rather novel.
(4) The proposed method advances the state-of-the-art results.

Weaknesses:
(1) The number of “linking token” is set to 2 in this paper (i.e. foreground and background) manually. However, the authors claim there exists heterogeneity in different regions of background. Is it reasonable to only set 1 token for background? 
(2) The demonstration of some notions are obscure, e.g. the dimension of   X_s^i and X_q^i should be explicitly pointed out for better understanding. 
(3) some details are missing or not explained.  e.g., how to transform a patch into pixel-wise mask map? What is the principle to generate discarded patches?



clarity,_quality,_novelty_and_reproducibility: The idea of suppressing the intra-class variation to cope with heterogeneity is quite interesting for FSS problem. The proposed 3 modules (i.e. cross-sample, cross-region, MIS) seems to be reasonable. The organization of this paper is clear. I believe it is not difficulty to reproduce the proposed method.

summary_of_the_review: The proposed method is interesting and rather novel, achieving appealing results. The paper is also well written and easy to follow.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The authors propose training feature extractor for Few show segmentation. To suppress the sample level, region levle and patch level heterogeneity between samples, the author propose Multi-level Heterogeneity Suppressing extractor (MuHS). MuHS uses attention  to suppress the heterogeneities by reinforcing the attention between query and support samples, regions and patches, and a novel masked image segmentation.



strength_and_weaknesses: The paper has the following main strengths:


1) The paper is well written and the main ideas are effectively presented in the paper.
2) The paper tackles a problem of interest to the community in FFS.
3) The paper presents a strong idea and is intuitively sound.
4) The paper shows significant improvemnet on the previous stat of the art.

The major weaknesses of the papers in my opinion are as follows:
1)The mehtod is only tested on two datasets. Have the authors tried more datasets to get a better idea of the performance.
2) The codes for the paper are not released.
clarity,_quality,_novelty_and_reproducibility: The quality of the paper is good. It is a well written paper. A problem of interest to the community is tackled in the paper. The main idea of the paper is novel and is intuitivley sound. 


Reproducibility: Codes have not been released.
summary_of_the_review: The paper proposes to use transformers to learn feature extractor for FFS. The authors claim that feature extractors have been overlooked in the FFS literature and off the shelf networks trained on imagenet are used as feature extractors. The authors propose to use transformers to supress inter-sampels, region and patch heterogeniety in FFS.

The method is tested on two datasets and the resutlts show significant improvement.

I would like the authors to address the following:
1)The mehtod is only tested on two datasets. Have you tried more datasets to get a better idea of the performance.
2) The codes for the paper are not released.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: In this work, the authors propose a new feature extraction framework called MuHS to improve few-shot semantic segmentation using multi-coarse heterogeneity-aware optimization. The MuHS is optimized with three key components: cross-sample attention, cross-region interaction, and masked image segmentation. The empirical results are sound to validate the efficiency of the proposed methods.
strength_and_weaknesses: Strength: 
1. This is a good engineering job. Few-shot semantic segmentation is an important but challenging problem. The proposed components in the framework could be useful utilities in various applications.
2. The paper is well structured with clear intuition and explanation.	

Weakness
1. Lack of novelty. The improvement is a bit incremental, although I do acknowledge the contribution of three components to few-shot segmentation. The main contribution is the application of some tricks in the few-shot segmentation task, which is an empirical advancement and the theoretical novelty is limited.
2. Although the paper has provided extensive evaluations over different metrics and datasets, some details of the experiment setup and results are not fairly convincing. For example, the setting of ablation studies is not appropriate, because an ablation study typically refers to removing some “feature” of the model or algorithm, and seeing how that affects performance. The results of ablation studies are not enough to validate the efficiency of the proposed components. 
clarity,_quality,_novelty_and_reproducibility: The technical novelty is limited in the intuition that it is just a superposition of mature methods. The cross-attention, triplet loss, and masked image segmentation are mature methods.
summary_of_the_review: This is a good engineering job. But the novelty is limited.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper tackles the Few-shot Semantic Segmentation (FSS) task with focus on learning the feature extractor. This paper thinks the FSS feature extractor deserves exploration and observe the heterogeneity (i.e., the intra-class diversity in the raw images) as a critical challenge hindering the intra-class feature compactness. In this paper, aothors propose Multi-level Heterogeneity Suppressing (MuHS), which utilizes novel cross sample attention, cross-region interaction and masked image segmentation to suppress the heterogeneity from three levels.

strength_and_weaknesses: Strength ：
The feature extractor has been overlooked by recent state-of-the-art methods. Accurately analyze the heterogeneity from three levels in the FSS task. This paper propose a feature extractor with Multi-level Heterogeneity Suppressing (MuHS). 
The paper according to the heterogeneity has three levels from coarse to fine leverages the attention mechanism in transformer backbone to effectively suppress all these three-level heterogeneity. Experimental results confirm that MuHS is compatible to multiple FSS heads and achieves new state of the art using a simple linear classification head.

Weaknesses：
Instead of designing a special classification header based on such a powerful feature extractor, a simple linear classification header is used.

clarity,_quality,_novelty_and_reproducibility: The quality of the work is very good, and it also provides new ideas for the subsequent work. The introduction of the overall structure and each module is also clear and original.
summary_of_the_review: 1、Can you provide a visual comparison of the feature maps before and after eliminating heterogeneity？Please add relevant experiments to make the paper more comprehensive.
2、Would the results be better if the classification headers were replaced with those of dense corresponding methods( (i.e., HSNet, DCAMA))?
3、The comparison results of convergence speed can be added.
4、The number of learnable parameters required by different methods also needs to be compared.
5、One of the goals of the article is to achieve the intra-class feature compactness. But subsequent experiments don’t prove this.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper initially received borderline scores, and was therefore additionally discussed in a virtual meeting with reviewers. Reviewers generally considered the paper to be solid, and that it proposed a reasonable approach to a useful problem, although there were some comments that the method is somewhat incremental. There were also initially some concerns about experimental evaluation and testing on too few datasets, however the author rebuttal added results on an additional dataset and overall addressed most reviewers concerns. Some reviewers raise their scores in light of the rebuttal. At the reviewer meeting, all reviewers present were in favor of accepting the paper. They only reviewer not present was also the only one with a score of marginally below the acceptance threshold; however, one of their main comments requested more datasets which the remaining reviewers and I felt was addressed with the author rebuttal. In summary, I agree with the majority reviewer opinion that this paper should be accepted to the conference.
justification_for_why_not_higher_score: Although the proposed method is solid and demonstrates effective performance, it does not provide significantly novel ideas to justify a higher score.
justification_for_why_not_lower_score: I do not recommend reject, since this paper presents a reasonable method for a useful problem, and provides solid experimental validation that was improved with an additional dataset during the author rebuttal period.
Decision: Accept: poster