Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: The paper addressed the inevitable data (and performance) imbalance across different categories in semi-supervised semantic segmentation framework by adaptively balancing the over- and under- performed classes. The proposed methods empirically showed good performance compared to other SOTA works on multiple benchmark datasets. 
main_review: The originality of the work is moderate yet solid. Imbalanced performance across classes can plague the performance of semi-supervised learning models and this work proposed a practical way to alleviate this issue.  In my opinion it deserves to be seen by the community. 

There are still a few questions that could be addressed in more details:

1. The predictions from the underperforming class by definition are inaccurate. By biasing to the low performance class, how to balance the negative impact of the inaccurate prediction with the adaptive equalization?

2. With normalized sampling probability from confidence banks, what is the exact way to sample images of a given class in adaptive CutMix and Copy-Paste. The introduction in the relevant sessions lacks details. 

The clarity of the presentation though clearly has room to improve. For example, Eqn (4) - (6), not sure why the same letter N is used in one equation to represent different concepts. 

limitations_and_societal_impact: No negative societal impact as far I know.
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: (1) The paper proposes a novel framework for semi-supervised semantic segmentation, namely adaptive equalization learning;

(2) The paper proposes two data augmentation strategies including adaptive Copy-Paste and CutMix to give more chance for under-performing categories to be copied or cut;

(3) The paper presents an adaptive equalization sampling strategy to encourage pixels from under-performing categories to be sufficiently trained;

(4) A simple yet effective re-weighting strategy is proposed to alleviate the noise problem in semi-supervised learning;

(5) Experimental results illustrate the effectiveness of the paper.

main_review: In this paper, the authors have presented the adaptive equalization learning for semi-supervised semantic segmentation, which aims at improving under-performing categories during training. Overall, I am satisfied with the paper and tend to accept the paper due to its novelty and strong experimental results.

**Strengths:**

(1) The paper is well-written and easy to follow; 

(2) The proposed method is novel and well-motivated;

(3) The proposed augmentation strategies utilize limited labeled data in a more efficient way;

(4) The proposed equalization sampling and re-weighting strategies are effective and can be applied directly to other semi-supervised semantic segmentation algorithms;

(5) Experimental results on Cityscapes and PASCAL VOC 2012 are promising and ablation studies are also helpful to understand the contributions of each component of the proposed method.

**Weaknesses:**

(1) In adaptive CutMix, how to determine whether an image contains a certain category?

limitations_and_societal_impact: The authors have adequately addressed the limitations and potential negative societal impact in the paper.
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: This paper tackles the challenging semi-supervised semantic segmentation task, with a focus on dealing with the imbalanced/long-tailed distribution. The authors propose two adaptive data augmentation strategies, one adaptive sampling schedule, and one re-weighting method to handle the long-tailed issue. The proposed method works well on two public standard benchmark datasets.
main_review: 1. Strength
- The proposed framework is intuitive, simple yet effective, achieving state-of-the-art performance on two benchmark datasets
- Extensive ablation analysis to study every single proposed component
- The proposed method not only works under low-data regimes, but can consistently improve the model performance in the high-data regime where the whole training set is available

2. Weakness
- Since this paper claims that previous paper do not take care of the imbalanced distribution issue in semi-supervised semantic segmentation while this one handles it, I think it will be better to break down the mean IoU to per-class IoU to see how the proposed method improve those tailed classes.
- Since VOC and Cityscapes only have ~20 classes, I wonder if the proposed equalization training framework will work better in other datasets with more classes (and thus the long-tail may be more obvious), such as COCO or ADE20k. It will be great if the authors can conduct some experiments, but not mandatory.
- I wonder if the pseudo labels (in L102) are one-hot or soft labels.
- From my understanding, the adaptive data augmentation is an image/path-level operator to balance the distribution, and the equalization sampling is at the pixel-level. While the data augmentation applies for both the labeled and unlabeled data, why is the equalization sampling only applied on the unlabeled data but not labeled data?

limitations_and_societal_impact: N/A
review_title: null

Meta-review: This paper offers an adaptive equalization strategy for semantic segmentation to improve performance of underperforming or underrepresented categories.  All reviewers found positive strengths in the paper, and recommended acceptance. The reviewers rebuttal provided additional information that were compelling and confirmed that this paper is acceptable, preferably as a spotlight.
Decision: Accept (Spotlight)