Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper presents a new design of the regression loss function for oriented object detection. When modeling the rotated bounding boxes as 2D Gaussian distributions, the authors introduced the Kullback-Leibler Divergence for oriented bounding boxes regression as an alternative to the commonly-used Smooth-l1 loss function. The experimental results show that the proposed loss function could obtain better performance on several benchmarks. 
main_review: - This paper extended the idea of modeling the oriented bounding boxes as Gaussian distributions [1]. The main difference between this paper and [1] is using the KL Divergence instead of the Gaussian Wasserstein Distance as the regression loss function for the learning of oriented bounding boxes. Although better performance could be obtained, this paper did not present insights for the reason why using KL Divergence.  Accordingly, this paper is actually another attempt for finding a better-performing distance function under the framework of Gaussian modeling of oriented object detection. Without further exploiting the insights behind the proposed KLD loss, the main contributions claimed about the novelty of the proposed loss function by this paper are not that significant.

- The authors discussed the **self-modulated mechanism** of the KLD loss, however, it is only a trivial observation. As the final evaluation metric is based on the IoU computation between two bounding boxes in the 2D Euclidean space, the more strict requirement for the objects with a larger aspect ratio should be taken into account for the sake of better IoU. A feasible solution is improving the weight for learning large aspect-ratio objects. 

- From another perspective, the proposed KLD loss couples the orientation and the width-height estimation together. As shown in equation (10), the residual in $x$ and $y$ directions are rotated by a rotation matrix $R(\theta) \in \text{SO}(2)$, which is actually a conversion from the polar coordinate system to the Euclidean one.

- The KLD loss only addressed the inaccurate angular regression of the **oriented** object detection for the larger aspect-ratio objects. For the detection of the horizontal bounding boxes, the problem of inaccurate regression for the width and height still remains.

## References
[1] X. Yang, J. Yan, M. Qi, W. Wang, Z. Xiaopeng, and T. Qi, “Rethinking rotated object detection with Gaussian Wasserstein distance loss,” in International Conference on Machine Learning, 2021.
limitations_and_societal_impact: The authors have adequately addressed the limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This paper focuses on learning high precision bounding boxes for rotated object detections. Instead of directly predicting the size of the box, they turn the box into a gaussian distribution and use the KL divergence between the gaussian distributions as the regression loss. They show that KLD is modulated where the optimization of one parameter will be affected by other hyper-parameters. For example, the gradients for the orientation involve the width and height of a box. Experiments show that the proposed loss function significantly improve the performance of a detector on different aerial image datasets and scene text datasets.
main_review: The main contribution of this paper is that it shows why KLD is a better option than existing losses to train a network for rotated objects both analytically and empirically. Their results also generalize well across different datasets. Although this paper does not propose any new loss or new formulation of a task, this shows a strong result for future works on rotated object detection. Furthermore, this paper does a good job on explaining everything very clearly. It is very easy to follow. Overall, I think this is a good paper.

One downside of this approach is that it does not have much advantage on horizontal object detection such COCO as shown in table 6, which limits its practicality, considering that some corner-based detectors [1] already perform well at high IoU level on COCO. Having said that, I do not consider this is a major issue as this works very well rotated object detection which is also valuable.

The authors show that they are able to achieve a higher performance with a smaller network. So I would suggest the authors to include the inference time of their methods and compare it with other works.

1. Hei Law, Jia Deng: CornerNet: Detecting Objects as Paired Keypoints. Int. J. Comput. Vis. 128(3): 642-656 (2020)
limitations_and_societal_impact: Yes.
review_title: null

final_score: rating: 2: Strong rejection
review_content: summary: The paper proposes an algorithm that can detect slender objects,
like text in images or boats in aerial images. The work mainly focuses on
the bounding box regression task and includes a parameter for box rotation.
Bounding boxes are represented as Gaussian functions instead of axis-aligned
boxes. The scale invariant KL-divergence loss is applied to the regression task.
An analysis of the loss function proclaims that the computation of gradients,
during optimization, are adjusted with respect to the bounding box parameters.
main_review: The paper is not reproducible, motivations and explanations
are incomplete. This paper is not yet ready for NeurIPS 2021.

1. The paper gives a vague description of the problem, why is it important
and what are the challenges. There is insufficient motivation. A clear
description of why and the final end-use should be included. In this end-use, why are rotated bounding boxes better compared to another high-
precision object representation, for example segmentation.
2. A clear motivation of why it is more correct to directly predict bounding
box parameters, rather then parameter offsets, is not given. Both can be
equally good depending on prior assumptions.
3. Line 229, what kind of data augmentation?
4. It is a strong claim to say that horizontal bounding boxes are a special
case of something else without any references. Equations (4) and (16) are
not very similar. There are large differences, more terms and also different
norms.
5. Missing references. Claims: lines 102, 104-105, 141-144, 212. Terms: lines
275, 276, 279.
6. Which methods do you refer to when you say they are not scale invariant?
In SSD, the offsets are very near a standard normal distribution. It is
unclear why a scale invariant loss versus a simple scaling of the loss is
beneficial.
7. You want to convey that the KL-loss dynamically adjust gradients w.r.t
width and height. However, the details do
not clearly explain this statement. E.g. is function f (13) defined here or explained somewhere else?
8. Usually L1-norm is better when there are outliers. Here outliers are allowed to contribute more to the loss, due to the quadratic terms in the KL-divergence loss. From the ablation this is solved by (18). This extra normalization questions if the KL-divergence is actually contributing
or simply produces noise in the results. This loss would have been better motivated if Table 6 was extended with more of the state-of-the-art
detectors, where axis aligned boxes are used.
9. References [6,7,8,9,10] do not include an extra log term in the bounding
box regression. In (4) and extra log term is added.
10. Line 63, not well motivated or explained.
11. Line 191, is f the same as in (13), (14), (15)?
12. Needs proof reading. E.g. line 38, (13), line 143, line 194, line 279.

Update after rebuttal:
several questions have been addressed in the rebuttal. However, question 8 is not sufficiently accurately addressed. Also: what does "We will discuss above in detail in the final version." mean concretely.
Given the responses from the reviewers, I have raised my assessment, but the paper is still not of sufficient quality for acceptance.

Update after discussion with the authors:
The disrespectful tone during the discussion is unprofessional and not suitable for a scientific top tier conference. The rating is changed accordingly.
limitations_and_societal_impact: It is not clear why the listed fields (line
288) are sensitive. An example for one of the fields could give a more complete
understanding of the negative impacts.
review_title: null

Meta-review: The reviewers have discussed the paper and have not come to an agreement.
Reasons to accept: the proposed approach is noted to be simple and yet effective, and it can improve the performance of various existing object detectors on different datasets significantly. Most of the issues raised in the reviews were properly addressed in the author responses (including extra experimental results).
Reasons to reject: some of the reviewers were not satisfied with the provided responses and decided not to support the paper.

I think that the overall pros of the paper outweigh the cons and would like to recommend accepting the paper.

Decision: Accept (Poster)