Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 10: strong accept, should be highlighted at the conference
review_content: summary_of_the_paper: This paper presents HBox-supervised oriented object detector.
H2RBox learns the rotation via self-supervised learning, whose loss measures the consistency of the predicted angles in two different views. 

strength_and_weaknesses: Compared to the alternative HBox-supervised instance segmentation methods, H2RBox achieves much higher detection accuracy especially for complex scenes, yet with lower memory and higher speed.
Compared with fully RBox-supervised algorithms, our method still shows competitive, and sometimes even better performance.
clarity,_quality,_novelty_and_reproducibility: --- 
In equ. 7, what are t^{*}_{ws} and t_{ss} denoted?

---
 It is still unclear why the combination of wsl and ssl can figure out the oriented boxes.
This is my main concern. 
The wsl branch may predict a lot of boxes with the same horizontal circumscribed rectangle, and the ssl branch tries to learn the same boxes as wsl with random rotation transformation.
summary_of_the_review: This paper has significant contribution to the community, and the results are quite good (very close performance and speed compared to rotated box-supervised methods).
 It may reveal that we can use HBox annotations than RBox one to learn oriented object detectors.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposed a method to detect oriented object with only horizontal bounding box annotations. In detail, it designed two losses. The first is the weakly supervised loss and the second is the self-supervised consistency loss. The proposed method outperformed the box supervised instance segmentation method such as BoxInst. It is also close to the oriented box supervised model.
strength_and_weaknesses: Strength
1. The direction is interesting and new.
2. The proposed method achieved very good performance (comapred to the previous methods and oriented box supervised models).

Weakness
1. The method is not very novel. For example using the horizontal circumscribed rectangles as supervision is similar to the techinical used in BoxInst[1]. The scale consistency loss is also widely used in previous methods, such as [2, 3].

[1] https://arxiv.org/abs/2012.02310
[2] https://arxiv.org/abs/1905.02249
[3] https://arxiv.org/pdf/2004.04581.pdf
clarity,_quality,_novelty_and_reproducibility: In general the quality of this paper is good.
summary_of_the_review: The problem studied in this paper is new. But the method is not very novel. But the performance is good. So I prefer to accept this paper.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposed a weakly supervised method for oriented object detection by leveraging only horizontal bounding boxes. The network is able to learn to predict the rotated boxes by building the transition functions between a WS branch on original images and a SS branch  on rotated images. By building upon the FCOS detector, the proposed method performs favorably against state-of-the-art weakly supervised approaches.
strength_and_weaknesses: Strength

-The main contribution that building the self-supervised learning strategy on a rotated image is interesting. 

-The experiments are sufficient to demonstrate the effectiveness of each component and the overall performance.

Weaknesses

-The authors mainly evaluate the proposed framework on FCOS, it's suggested to verify the model's generalization on other baseline methods for general object detection or oriented object detection. 

-The authors used several loss items with empirical trade-offs. It's necessary to explain the reasons and show their effects on final performance.
clarity,_quality,_novelty_and_reproducibility: This paper is well originated with interesting ideas and convincing results.  
summary_of_the_review: This paper brings some new insights for weakly supervised oriented object detection. Currently, I'm glad to accept it.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper proposed an oriented object detector that used horizontal box annotations. The proposed method applied weakly- and self-supervised learning for predicting the angle of the object. The experimental results on standard benchmarks show the effectiveness of the proposed method.
strength_and_weaknesses: ## Strength:
### (1) The proposed method is interesting since it can predict the angle of the object without the supervision of the angle.
### (2) The experimental results show that it can achieve competing results compared to the fully-supervised methods.

## Weaknesses:
### (1) The proposed method need more GPU memory than previous methods, as listed in Table 1.
### (2) The definition of the angles of the objects are quite fusing for me. For example, a horizontal box that exactly match the object can be either 0 degree or 90 degree. How to distinguish them?
### (3) The contributions listed in the Introduction Section are weak since two items of them are the experimental results. It would be better to specify the technical contributions instead of the experimental results.
clarity,_quality,_novelty_and_reproducibility: The writting of this manuscript is okay. The proposed method is novel. The reproducibility is okay if the code is released.
summary_of_the_review: Considering the strength and weaknesses of this paper, I tend to give a positve recommendation.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper studies the oriented object detection. Different from existing works, it explores a weakly-supervised setting where only horizontal bounding box annotations are provided. To address this problem, it introduces a new method that leverages self-supervised learning method (enforcing the orientation prediction consistency of the object with different views). Experiments demonstrate the proposed method outperforms existing works significantly.

Strength:

- This paper explores a useful setting for oriented object detection. 
- The proposed method is reasonable and easy to follow.
- The proposed method performs quite well in the experiments.

Weakness:
- The novelty is kind of limited in the aspect that this work does not introduce new method/components. 
justification_for_why_not_higher_score: The reviewers share the comments that this paper has limited novelty. It does not provide new method or model component design. Instead, it integrates existing components/methods into a working-well framework. This framework is valuable for the community. But the scope of this work and the limited technical insight limits its impact for the community. 
justification_for_why_not_lower_score: The framework proposed by this paper works well. It is reasonable and valuable for the community. All the reviewers agree with accept. The reviewers raise some questions about this work and the authors address them well in their response.
Decision: Accept: poster