Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: summary: This work proposed a new IoU based loss function-\alpha-IoU for object detection. The proposed \alpha-IoU can be integrated with existing IoU-based losses, e.g. GIoU, DIoU, and BIoU, and could further improve the performance. The author studied the property of the proposed method and evaluate the proposed method on several standard benchmarks.
main_review: Pros: The writing is good and the paper is well organized. The idea of adding the power term into the IoU loss is new for me. And extensive experiments on standard benchmarks are conducted. 

Cons: 
- While adding the power term to the IoU loss is new, but its motivation is not clear and straightforward. In addition, the improvements compared with the baseline methods are not significant, especially in MS-COCO dataset, and the performance gain has quickly shrunk on the stronger detectors. As shown in Table 1. 

- As L285-L286 stated, \alpha < 1 consistently performs worse than the baseline model. This indicates that the proposed method mainly increasing the importance of the well-localized samples. Therefore, there are some competitor methods are not compared, such as <Dynamic R-CNN>, <Focal and Efficient IOU Loss> and <Revisiting the Loss Weight Adjustment in Object Detection

limitations_and_societal_impact: No
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: In this paper, the authors propose a simple but effective IoU loss. By adding a power factor on the IoU loss, the network focus more on proposals with high IoUs with ground truth. Experiments of various object detection baselines on two datasets validate the effectiveness of this loss function.

main_review: I have some concerns about applying the proposed transformation on other IoU loss variants. Besides IoU loss, the authors also add the power factor on other IoU loss functions. However, I find some inconsistencies on these loss functions. The IoU is a metric which is better when it's higher. But other metrics, such as the center distance in DIoU and aspect ratio difference, are better when they are lower. The power parameter then have different meanings on these metrics. By setting alpha>1, better bounding boxes have higher weight on IoU, but will have lower weight on other metrics. This property seems to be conflict with the authors' motivation.
If the authors could fix this issue, I am satisfied with the approach.
limitations_and_societal_impact: See above.
review_title: null

final_score: rating: 4: Ok but not good enough - rejection
review_content: summary: Following the success of IoU-based regression losses for bounding box localization, this paper proposes a family of power IoU losses, where the IoU-related terms in these losses are regularized by a power parameter \alpha. This idea is a direct translation of what is used in Generalized cross-entropy loss [37] and Focal loss [19] for IoU-based regression losses. By applying this new loss variant on several recent object detectors are claimed to improve their detection performance on the popular detection dataset and using the mAP and AP metrics.
main_review: The paper's idea is well clear and it is easy to follow. I have few major and critical concerns about the contribution's originality and significance, lack of theoretical proof for the claims and experimental fairness: 

1- Originality: This idea can be seen as a direct translation of what is used in Generalized cross-entropy loss [37] and Focal loss [19] for IoU-based regression losses and I don't see a new existing research idea 

2- lack of theoretical proof and false claims: First of all, this \alpha modification of IoU and GIoU is claimed to hold the metric property of IoU and GIoU losses, (The other IoU loss variants, e.g. CIoU, DIoU etc, may not be even metric losses. ). I don't see a proper mathematical proof fulfilling this claim in the main text or supp. materials. The authors have a sentence in line 155 page 4 mentioned that the power transformation preserves key metric property and cite the paper [28]. I didn't find such proof in the GIoU paper for the power transformation. Property 1 might be helpful for proving this lemma. But even property is only a naive claim and no mathematical proof is available.

I am also not fully convinced that if we optimize a loss (\alpha-IoU) that is not directly correlated with IoU as a metric, it would be useful compared to optimizing the metric. why it makes sense to use a loss it has a high gradient in high IoU and a very low gradient in low IoU. More insightful theoretical analysis is required to support the usefulness of the proposed losses

3- Experimental fairness: First of all, I couldn't find the detailed implementation details for all the experiments in Tables1-3 and how hyperparameters are set for the baselines. I wonder this improvement is rather a side effect of dynamic weightings against classification losses over iterations rather than the effect of the regression loss itself. Obviously, the Intersection over Union between prediction and GT boxes increases over the training iterations makes the regression loss bigger in the case of \alpha-IoU. Detection is a multi-task problem and regularization between the regression and classification losses matters considering their effect in AP and mAP metric.   More detailed experiments are required to substantiate the source of the improvement 
limitations_and_societal_impact: Better localization of objects might be conducive to reduce the risk of collision in autonomous driving applications.
review_title: null

Meta-review: The submission considers the use of a power term in the IoU for bounding box regression.  By exponentiating the IoU in the construction of the loss, the gradients will be higher for high/low IoU depending on whether the parameter is greater or less than one.  The reviewers were unanimous that the paper is not suitable for publication at NeurIPS at this time, and the opinion remained during the post rebuttal discussion.  Among the main concerns about this paper were: that the reported benefits of the method decrease with stronger detectors and are small compared to e.g. Dynamic RCNN, and that the idea is quite similar to generalized cross-entropy loss and Focal loss limiting novelty.
Decision: Accept (Poster)