Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper formulates the instance segmentation problem as a graph partitioning problem, and uses reinforcement learning to predict the edge weights, which are the inputs to multicut solver for object segmentation. The authors introduce a strategy for spatial decomposition of rewards based on fixed-sized subgraphs to generate the object-to-sub-graph reward mapping to guide the action prediction. The experiments on toy and real data demonstrate that a good set of priors is sufficient to reach excellent performance without any direct object-level supervision.
strength_and_weaknesses: Quality/Clarity: the paper is well written and the techniques presented are easy to follow. Its motivation to use reinforcement learning for instance segmentation is clear, such as the non-differentiable step of instance extraction and lack of annotated training data.

Originality/significance: the idea is interesting, and there are multiple strengths:  (1) superpixels and graph neural network feature exaction (2) sub-graph reward mapping. However, it is a little weird to add reinforcement learning here. Additionally, the authors claim that there is no state transition. Without the buzzword "reinforcement learning", I think we can achieve similar results using the designs (GNN+reward mapping) in this paper.
clarity,_quality,_novelty_and_reproducibility: I do not think it is necessary to add reinforcement learning here.
summary_of_the_review: The paper use actor critic to predict the edge weights and then use graph partition for instance segmentation. Overall it is a good paper, but not ready for publication.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: - The paper formulates instance segmentation as a RL problem based on a stateless actor-critic setup, achieving end-to-end learning while exploiting prior knowledge on instance appearance instead of using ground truth instance supervision.
- The paper introduces a strategy for spatial decomposition of rewards based on fixed-sized subgraphs to enable localized supervision from combinations of object- and image-level rules. 
- The paper demonstrates the feasibility of the approach on synthetic and real images and delivers excellent segmentations without ground truth supervision.

strength_and_weaknesses: Strength:
The paper is well-motivated and well-written.
The overall RL environment looks sound and reasonable.
The experiments show the feasibility of the method.

Weakness:
The first paragraphs of the section 3 method, which, in my opinion, tries to describe the RL formulation of the problem, is hard to grasp overall, especially on how the image is segmented by graph partition.
Although the experiments show the feasibility of the approach, the method is quite limited to segmentation problems with strong priors. Therefore, the potential of expanding the idea to more real-world datasets is quite unclear, thus, it makes the contribution limited.

clarity,_quality,_novelty_and_reproducibility: Can you provide an evaluation of the quality, clarity, and originality of the work?
Clarity: the paper is well-presented and the experiments are clear.
Quality: Please refer to the strengths.
Originality: the paper brings prior knowledge into end-to-end learning via using RL
Reproducibility: we’ve known that RL is hard to train, moreover, with GNN in the MDP, in my opinion, it seems hard to make the model converge. I recommend the authors provide more implementation details.

summary_of_the_review: The paper is quite novel but the contribution is fairly limited.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper explores the instance segmentation via actor-critic reinforcement learning. Instead of large amount of object-level supervision, the author formulate the instance segmentation as graph partitioning and predict the edge weights driven by the reward from object shape, position and size. Experiments on toy and real data demonstrate the effectiveness of the methods with a good set of priors. 
Overall the proposed algorithm provides a new view regarding prior knowledge embedding, which enable well trained model with less annotated labels. 
strength_and_weaknesses: Strength:

+Training model with non-differentiable factors. When dealing with limited samples, the reinforcement learning plays a key role and the author take the advantage of it.

+The experiments is interesing and complete. They are conducted on both synthic and real images. 

Weakness:

-The reason for utilizing the superpixel as the prior is not well explained. 
-The estimation to computatial cost and inference speed (e.g., FLOPs, FPS) are not presented in the experiments. 

-Comparisons to standard instance segmentation are missing. Although the algorithm has the advantage in limited samples, it doesn't provide enough avidence to demonstrate the compatitive scores when the training samples are adquated

-Experiments on real dataset is too simple to indicates the potential ability. Authors should provide the results on more complicated medical images.
clarity,_quality,_novelty_and_reproducibility: The paper is generally clearly written. However, the clarity of the methods could be improved. See the weakness above. 
There lack extensive experiments on public instance segmentation benchmark datasets,
The novelty is limited due to lacking the comparisons on more complicated medical dataset. 
The experiments can hardly reproduced because the lacking of detials on superpixel graphs, which is the determine factor to the algorithm.  

summary_of_the_review: This paper address the limited labels for instance segmentation thorough actor-critic reinforcement learning. It follows the standard RL framework and take the object shape, position and size as the prior. By assigning the reward for each actor, the optimal instance score is obtained. 
Overall the paper is well origanized but the experiments are not adquate to demonstrate the effectiveness of the algorithm. The reviewer suggest the author conduct more experiments on mote compilicated benchmark for instance segmentation.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: All reviewers for this paper concurred that this paper presents an interesting method. However, reviewers expressed concerns about the relatively limited scope of the contribution and the corresponding evaluation. There was also some question of whether the reinforcement learning-based approach makes the most sense and provides sufficient value given the additional complexity added. Overall, all reviewers concurred that the paper in the current form, while interesting, is below the acceptance threshold for ICLR. After reviewing the paper and author and reviewer comments, I agree with the shared assessment of the reviewers and do not recommend acceptance of this paper at this time.
justification_for_why_not_higher_score: The limited scope of the contribution and the corresponding evaluation is the primary reason that acceptance is not recommended.
Decision: Reject