Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The authors proposed an intermediate prototype mining transformer method for few-shot semantic segmentation. According to experimental results on two widely used datasets, the proposed method achieves promising performance compared with other state-of-the-art deep learning models. 
strengths_and_weaknesses: Strengths:

1. The paper is overall well organized. Authors systematically introduced the related works, limitations, and potential improvement solutions, which are very helpful to direct reviewers/authors to the specific topic of this paper.
2. The proposed method is well described, especially the functions and advantages of each added module, the organized codes further make this study easy to follow.
3. The visualization in the manuscript and supp files are well complementary to the quantitative metrics listed.

Weakness:

Even though it's a very good point that the proposed method performs consistently better than the compared deep learning models on two datasets, I wonder if the added performance is based on larger-scale parameters of the proposed model than the existing ones? So, it would be better to include a discussion/comparison about the number of parameters of both the proposed and compared methods.
questions: A basic standpoint of the proposed method is intra-class diversity counts for the few-shot segmentation task. Even though sine scatter plot visualizes the diversity in a subjective way, it's more reasonable to introduce some objective metrics or evaluations for diversity.
limitations: The authors have adequately addressed the limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper deals with the few-shot semantic segmentation problem. Instead of aggregating support category information as prototype, this paper proposes using an intermediate prototype to encode the semantic information from both support and query. By this way, this paper aims to reduce the intra-class discrepancy between support and query. The query features and the learned intermediate prototype are concatenated, followed by 1x1 and 3x3 convolutions, to generate the query mask prediction. The intermediate prototype and the query mask prediction are iteratively refined and improved. Experiments show the effectiveness of proposed method. 
strengths_and_weaknesses: Pros:
- The idea is reasonable.
- The experiment results are good.
- The paper is easy to follow. 

Cons:
- The idea to iteratively refine the query prediction is proposed by previous works, e.g. CANet [1]. And the QA module is similar to some design in IOM of CANet. Thus, those two parts, in my opinion, have quite limited novel contributions. 

- The authors adopt separate weights for support and query attention module in IPMT. I would like to see the result when the weights are shared across support and query attention modules. 

[1] CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning, CVPR 2019.
questions: see the weakness part.
limitations: The authors didn't discuss the limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes using the intermediate prototype iteratively generated by Transformer (Intermediate Prototype Mining Transformer, IPMT) to guide the query segmentation. Specifically, it considers to mine category information from both support and query so that the query feature could be better activated. Experiments are performed on the commonly used Pascal-$5^i$ and COCO-$20^i$ datasets. 
strengths_and_weaknesses: **Strengths**
1. The motivation of exploring intra-class diversity between support and query makes sense to me. 
2. The generating process of the intermediate prototype is well explained, and the iterative design of IPMT is reasonable.
3. According to the experiment part, the proposed method obtains significant improvement compared with the previous method. 

**Weaknesses** 
1. The baseline in Tab.4 and Tab.5 is not clear. For Tab.4, are DSL and QA applied? For the baseline of Tab.5, it refers to PFENet [28] in line.298, which seems not true according to the description and the result.

2. Since the proposed IPMT processes support and query symmetrically, why would the produced intermediate prototype bias towards the query (as Fig.1 and Fig.4 shown)? 

questions: See Weaknesses for most questions. 

Please address these questions in the rebuttal, especially point 2. I feel confused about why the prototypes would bias toward the query in the symmetrical design. Is it because the prototypes absorb the context information of the query image during the coarse-to-fine procedure?

Besides, some minor points that could be improved:
1. The paper only explains the setting of the 1-shot but does not explain how to extend to K-shot settings.
2. SDL->DSL at the line.296

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EDIT  
The rebuttal resolves my concerns and also provides some interesting analyses. I would like to slightly increase my score, from 6 to 7. 
limitations: Limitations were addressed in the Supplementary.
review_title: null

Meta-review: metareview: All reviewers lean to accept this paper and this is a clear acceptance.
confidence: Certain
Decision: Accept