Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: This paper proposes a novel method on weak-shot semantic segmentation, in which fully-supervised model, MaskFormer, with dual similarity transfer was employed. The basic idea is totaly diffrent from the existing method of weak-shot segmentation, RETAB, which is based on the WSSS method. The experimental results showed the effectiveness of the proposed method.
strengths_and_weaknesses: Pros) 
 + New approarch for weak-shot segmentation task
 + The results by the proposed method outperformed the baseline, RETAB.

Cons)
 + The paper is very hard to understand. It is unclear why the proposed method works well.
 + Only one baseline, RETAB, was used. No meaning to compare with WSSS in which no pixel-wise annotation was used at all.
questions: 1) Why sampling only 100 pixels (J=100) is enough ? It seems to be very small compared to the total pixels of an image.
2) \gamma was examined in the ablation studies. Where \gamma was used? Eq.5 does not contain \gamma. 
3) The reviewer is not sure why the binary mask estimation model trained with base classes are applicable for novel class.
4) It is unclear how to train the model using base classes which have pixel-wise annotation and novel classes which have only image-level labels.
limitations: In Sup. Sec.8, the limitation is writen concretely. Since the limitation discussion is important, part of it should be included in the mail text.
review_title: null

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: This paper focuses on the weak-shot semantic segmentation problem which consists of two groups of categories, i.e., one with both annotated categories and masks, while another one with only labeled categories.  For predicting precise segmentation masks, this paper proposes a dual similarity transfer network based on MaskFormer. Experiments are conducted on the COCO-Stuff-10K and ADE20K datasets to show the effectiveness. 
strengths_and_weaknesses: Strengths:
+ This paper is well organized and easy to follow.
+ This paper proposes to use a small network, i.e.,SimNet, to learn the pixel correlations between pixels within or across images with mask annotations, and further, SimNet is applied to estimate the pixel relations of images without mask annotations. Although this design introduces extra computations, this is effective in addressing the novel classes. 
+ The experiments show that the proposed method can successfully improve the segmentation accuracy.

Weaknesses:
- According to the details in Section 4.2, pseudo-labels of novel classes generated by CAM are produced in advance. These pseudo-labels are utilized in the training process. However, the authors do not mention this in the method section. I think this part is crucial and should be included.
questions: 1. Line 162 - 164, 'Under ... produce a binary similarity with all the pixel embeddings.'.   Please explain how the similarities are learned and related to the supervision.
2. Is the SimNet trained in an end-to-end manner?
2. Line 187, what are the values of R_n ?
3. In Section 3.2, the notations should be specifically explained, e.g., i, w,h,c

limitations: Yes, limitations are adequately addressed.
review_title: null

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: This paper proposes a dual similarity transfer framework based on MaskFormer for the Weak-shot Semantic Segmentation task. It consists of the proposal-pixel similarity transfer and the pixel-pixel similarity transfer. Experiments are performed on the COCO-Stuff and ADE20K datasets.
strengths_and_weaknesses: 
**Strengths**
1. The results look good, and evaluations are extensive.
2. Considering the use of similarity transfer for segmentation makes sense to me. 

**Weaknesses**
1. The presentation of this paper is poor.
- In view of the contribution of this work, the merit of the proposed work is unclear. For instance, in Line 77, contribution 1: "...(dual similarity transfer framework) is effective for proposal-pixel similarity transfer". It is meaningless since the "proposal-pixel similarity transfer" is one of the components of the dual similarity transfer framework.  

- The method part is also hard to follow. It seems that this paper attempt to put the approach under the concept of similarity transfer [4]. However, the presentation of the method part mixes too many unrelated implementation details with the explanations of the design, making it hard to understand the reasonableness of the method. Based on my understanding, the proposed framework uses Maskformer as the base segmentation network, where the mask loss for novel classes is removed to fit the problem setting. It is a reasonable baseline, but I don't see a clear relation with the idea of "similarity transfer." Could there be more explanation about why it is called proposal-pixel similarity transfer?  
 Besides, symbols are not reflected in Figure 2, making it hard to correspond with the text description. And there are too many natural language descriptions in Section 3.4. I think a formal mathematical description is needed to help understand how the method works.  
  In addition, it seems that the proposed approach explicitly utilizes the "ignore labels" for learning masks of novel categories in Pixel-Pixel Similarity and Complementary Loss. I wonder whether it would cause the mask information leakage of the novel classes? If the novel and base classes do not appear in the same image, would the Complementary Loss fail to work? From another view, the results are close to the FullyOracle result on some split. Is it because of the possible information leakage?

- Quality of the figures is poor. Texts with light color in Figure 1(b) can not be seen clearly, and all figures would be blurred when zooming in. 

---
EDIT  
Although this work is somewhat incremental, I believe it is a meaningful attempt in WSSS with moderate-to-high impact in this sub-area. Therefore, I have increased my score from 4 to 6. The presentation may be further improved to make it easier to understand. 
questions: See questions in the Weaknesses part. Although the idea seems interesting, the presentation of this paper makes it boring to read, and it is hard to tell the approach's merit and reasonableness. 

limitations: Limitations are discussed in the paper.
review_title: null

Meta-review: metareview: Two reviewers give a weak accept rating while the other one gives a borderline reject rating. Considering the low confidence of the negative comment and the contrary comments in paper writing (confident "easy to follow" vs. unconfident "hard to understand"), the AC would lean to accept this paper. 
confidence: Certain
Decision: Accept