Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper presents an overall pretraining approach for object detection with a fixed pretrained backbone. A transformer-based detector is used.
The main contribution of the paper is the approach to selecting positive examples in a contrastive learning method. 
strength_and_weaknesses: Strength:
- The overall paper is well-written and defined. It is easy enough to follow and reproduce

Weaknesses:
- The reasoning for the design choice of a transformer-based detector is not well-supported. The paper's claim of the high amount of object proposals is suitable for contrastive learning. How about other types of detectors or considering low-score predictions as object proposals?
- Considering overlapping objects as other positives may not work well in other cases. How about crowd scenes?
- It is not proven the paper's claim of less costly than previous approaches.
clarity,_quality,_novelty_and_reproducibility: The overall quality of the paper is below the acceptance threshold since there are some aspects of the paper's claims not well-supported by theoretical and experimental analysis.
The originality of the work is not significant as the contribution is limited to positive examples selection for contrastive learning.
summary_of_the_review: My recommendation rating for the paper is to reject. The main reason is that some claims are not well-supported as identified in the weaknesses.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper proposes a novel contrastive pre-training strategy for object detection. The method uses object proposals generated by Selective Search for contrastive learning, which avoids large batch requirement in the existing work. And the localization of the proposals are considered during sampling stage, to further improve the training efficiency. The proposed strategy shows clear improvement over baselines.
strength_and_weaknesses: The paper is easy to understand and the experiment are very comprehensive. The idea of using localization information to sample proposals for pre-training is interesting to me. Overall speaking I think it's a good paper. 

I have two questions of the paper: (i) what's the training time of the proposed method compared with the existing work. In addition to large batch training, the contrastive pre-training is also time-consuming; (ii) the proposed pre-training model is used for fsod, andI feel the proposed strategy is general and can also be applied in generic object detection. What's the performance for generic object detection? 
clarity,_quality,_novelty_and_reproducibility: The writing of the paper is clear and the idea is interesting to me.
summary_of_the_review: Overall speaking I think it's a good paper.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes to solve the challenging large batch size for contrast learning in unsupervised object detection pretraining. They conducted experiments with the Transformer-based detector and found that the diversity of proposals generated by the detector can effectively mitigate this problem. Thus, they propose ProSeCo, which improves the state-of-the-art on the fewer data pretraining task. They also propose a novel localization-aware proposal matching loss and apply it in the feature-consistent student-teacher architecture, which will be very useful for object detection. I think the precision of the proposed method is obviously improved, and the pre-training for unsupervised object detection deserves more in-depth study.
strength_and_weaknesses: Strengths
+ The contributions are clearly illustrated and validated. The ablation experiments clearly reflect these contributions. I think ProSeCo is on the right way of exploration.
+ The illustrations and explanations are clear.
+ The proposed method outperforms others by a large margin.


Weaknesses
- In the Introduction, after analyzing the local feature consistency, there is no description of its application and advantages, and the relationship with the whole approach is not clearly elaborated.
- The red part in Figure 3 is not clearly explained, which raises confusion.
clarity,_quality,_novelty_and_reproducibility: See Strength And Weaknesses
summary_of_the_review: See Strength And Weaknesses
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: Paper was reviewed by three reviewers. While initial reviews were not as positive, after the rebuttal, all reviewers are arguing for acceptance with: 1 x accept, good paper and 2 x marginally above the acceptance threshold. Concerns of both cxY8 and 9EDD were appropriately and fully addressed; Sfkk has not responded to the rebuttal. Overall, reviewers agree that the paper is well written, contributions are clear and performance is substantially better than the baselines. AC has looked at reviews, rebuttal and following discussion and concurs with reviewers that paper would make a valuable contribution to the conference. Authors are encouraged to address remaining comments (e.g., from cxY8 with respect to additional comparisons) for the camera ready. 
justification_for_why_not_higher_score: To me the paper was between Accept (poster) and Accept (spotlight). I've decided to go with a more optimistic recommendation. 
justification_for_why_not_lower_score: Paper is well written and proposes both interesting and practical approach for an important problem.
Decision: Accept: notable-top-25%