Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This article presents a new loss (KFIoU) approximating the Skew Intersection over Union Loss (SkewIoU) for the rotated object detection problem. This loss is composed of a scale-insensitive center point loss, and a second distance-insensitive term using Gaussian modeling
and Kalman filtering. Compared to other Gaussian modeling based losses like the GWD and KLD losses, this loss is hyperparameter free, and is the first one used in a 3D environment. It is also the closest to the SkewIoU loss, and produce state-of-the-art results.
strength_and_weaknesses: Strengths: The method proposed is in the logical continuation of Gaussian modeling based losses. Its main advantage is a seemingly closer approximation of the SkewIoU dynamics, while being hyperparameter free.

Weaknesses: The method presented is really similar to the KLD loss, and present only very marginal improvements on DOTA (when using its center loss), and present no comparison with KLD for the other datasets of Table 2 and 3. 
The choice of using Kalman Filter (over the previous Gaussian modeling based losses) to create this new loss is never truly motivated. It is not justified why this method should result in a more physically reasonable loss, or one that is theoretically closer to the true SkewIoU value (other than using a similar formula than IoU). The experiments justifying the "alignment" between the losses are not really convincing either (see below).
clarity,_quality,_novelty_and_reproducibility: The article can be quite unclear, especially on the motivations behind this method. Neither a theoretical nor an experimental reason is ever clearly given on why the KFIoU loss should be clearly chosen over KLD or GWD loss.

As such, there is only limited novelty in this work compared to previous Gaussian based losses. The comparison of the KFIoU loss to the 3D object detection case is indeed novel; however without comparison to the KLD or GWD losses, this contribution is difficult to judge.

The constant pointing of SkewIoU as being "gradient-training unfriendly" is never truly explained or motivated. Even in the case that SkewIoU is particularly difficult to implement/backpropagate with, the main losses KFIoU is being compared with are losses such as KLD and GWD, and not SkewIoU.

The use of a scatter plot for Fig 3 c is questionable for its clarity. Fig 3 a-b do not seem to showcase a particular improvement of the KFIoU loss over the GWD and KLD losses. It is also unclear why no values of EMean are presented compared to EVar in Table 1. As such, it is difficult to truly conclude on comparisons between GWD KLD and KFIoU losses.

There are many spelling and grammar mistakes in the articles. Some examples are: "Gaussian modeing" (abstract), "works better due
to fully differentiable" (highlights), "are rarely SkewIoU loss" "due to the hard-to-implement
of the SkewIoU" (related work).
summary_of_the_review: This article presents a possibly interesting evolution of Gaussian-based SkewIoU approximating losses. KFIoU does not need hyperparameters and has some encouraging experimental results.
However, the method clearly lacks motivation, either theoretically or emperically, to support its lack of novelty compared to previous losses. Furthermore, the paper is often unclear, lacks critical comparisons with KLD and GWD, and with unsubstantiated claims (over SkewIoU "unfriendliness" for example).
For these reasons, I am leaning toward rejection.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The submitted article proposes a practical solution to the rotated object detection problem. To locate arbitrarily oriented objects in images, detectors often rely on the calculation of the SkewIoU. However, its closed-form calculation cannot always be provided and its implementation in popular deep learning framework is limited due to the missing implementation of custom functions. In this context, the authors propose a fully-differentiable approximation of the SkewIoU based on Kalman filter, called KFIoU. Extensive experiments show its practical effectiveness, both in 2-D and 3-D object detection problems.
strength_and_weaknesses: The paper provides an efficient and practical implementation of a loss function for rotated object detection. Such a loss, called KFIoU, does not require any additional hyperparameters and can be easily implemented with fully differentiable functions in existing deep learning frameworks. However, limitations of other implementations are not sufficiently discussed. The authors could insist further on such limitations in order to highlights the advantages brought by the proposed KFIoU. 

Some parts can also be rephrased and typos are to be corrected. 
In the abstract:
"modeing" -> modeling
"which is used to quickly get the center points between bounding boxes closer to assist the second term" -> it is not clear what you mean by that, especially I don't get what "closer" refers to.
In section 1:
"Rotated object detection is an relatively emerging" -> is a relative ...
"The highlights are as follows" -> the following
2nd point: the first phrase is redundant and refers to highlight number 1.


clarity,_quality,_novelty_and_reproducibility: The article proposes an extensive review of state-of-the-art approaches for rotated object detection. This gives the reader the context and gives clarity to the paper. 

The authors propose an approximation of SkewIoU, called KFIoU. Such term being fully differentiable, it is easier to implement. This answers to a practical problem, thus the novelty is limited.

Reproducibility is ensured by the fact that the authors will make their implementation publicly available.
summary_of_the_review: The paper, although not introducing a new approach to rotated object detection, solves the practical problem of implementation of the SkeweIoU by introducing the KLIoU. Effectiveness of the proposed term is validated by extensive experiments. Although the novelty is limited, the work has a practical impact.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: Training deep models to correctly detect and outline rotated objects is difficult due to the nature of the metric of reference (SkewIoU). The authors propose KFIoU, an easily computable and differentiable loss that relies on transforming bounding boxes into Gaussians and estimating the overlap between the Gaussians analytically. Experiments demonstrate both that the derived proxy loss follows SkewIoU trends fairly well, and leads to strong performance on both 2D and 3D benchmarks.

strength_and_weaknesses: Strengths:
- KFIoU is an easy to compute and differentiate loss with well principled motivation.
- KFIoU is hyper-parameter free and fairly simple to implement.
- The paper is mostly well-written with clear illustrations (notably Fig. 2) that sum up the problem and solution.
- Experimental results seem to validate the efficiency of the method with regards to previous approaches on both 2D and 3D detection tasks.

Weaknesses:
- No indication of uncertainty or context is given on the results. What is the standard deviation? How many runs were performed for each result? Are the baseline results reported or reproduced? If reported, are the settings comparable?
- The center loss is simply added as a correction term. This seems to work well, but it does not really reflect the product of Gaussian at the center of the paper. Have the authors tried using the distance between centers as a multiplicative factor on the KFIoU?
- This is more of an issue of semantics, but I am unconvinced by the paper’s positioning around Kalman filtering. At its core, KFIoU computes the overlap between two Gaussians through a product of the pdfs which is well known to also be a Gaussian pdf up to a scale factor. While this procedure is indeed very important in Kalman filtering to reconcile observations and predictions, it is just a small part of the entire process. As such, I feel like the paper’s reliance on Kalman filtering detracts from its actual key points.
clarity,_quality,_novelty_and_reproducibility: The paper is well written for the most parts. There are a few typos (e.g. equation (6)’s second term is incorrect as is, and is probably unnecessary as well), and some structural changes might benefit the paper (e.g. wait until eq. (7) to discuss bounding box volume, write out explicitly the full loss in a dedicated equation).
As discussed in the weaknesses, I think the paper overall suffers from leaning so heavily on Kalman filtering. Even without making use of Kalman filtering, I feel like the contribution of the paper is significant enough.
Miscellaneous comments:
- Why not show the EMean in Table 1?
- Is the upper bound on the value of KFIoU really important? An estimation of the difference between SkewIoU and KFIoU – if attainable – would be much more valuable.

summary_of_the_review: KFIoU is an interesting and easy to implement proxy for SkewIoU that follows easy to understand principles. Experimental results show the loss outperforms much of its competition and even sets a new state of the art on the DOTA dataset. As such, I recommend acceptance for this paper.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The authors proposed a new loss, called KFIoU, for the detection of rotated objects. Based on Kalman filters and Gaussian modeling, this loss aims at approximating the SkewIoU loss, which is unfriendly to differentiable learning. Formulated this way, the SkewIoU loss does not rely on any additional hyperparameter, contrary to its counterparts. Moreover, experiments show that the proposed loss is nearer to the SkewIoU loss than the other approximator counterparts of the literature in terms of error variance.
strength_and_weaknesses: Strength: 

Contrary to other SkewIoU loss approximations in the literature (GWD/KLD), the proposed KFIoU loss does not involve any new hyperparameter. 
 
The proposed KFIoU loss enabled the authors to reach competitive/SOTA results on the DOTA dataset. The ablation study show that the FKIoU loss achieves better results than the smooth L1 loss on three detection tasks (aerial, scene text, and face).
The results on the 3D detection seem also promising.
 
Weaknesses: 
 
Some results do not seem fair compared to SOTA.
Table 1 shows that GWD and KLD reach better results than the smooth L1 loss on the DOTA datasets. However, in the ablation study of table 2, where the approach is evaluated on the other 2D datasets, the proposed loss is only compared with the smooth L1 loss. Why not comparing with GWD and KLD ?
In Table 5, when comparing the 2-stage approaches, the backbone used is different from all the other works, preventing the fair comparison. One can ask if the improvement is due to the loss, or to the backbone.

Be careful with the proofreading, some errors complicate the reading (e.g. logical connectors in the beginning of the abstract).

The limitation mentioned in the appendix should be highlighted in a dedicated section, before the conclusion, for example.

clarity,_quality,_novelty_and_reproducibility: 
The authors proposed a novel approach, focusing on the loss function. The use of Gaussian-modeling-based losses to approximate the SkewIoU loss is not new, but the use of Kalman filters for this use is, and seems to be relevant.

One of the major arguments of the authors is that their proposed loss does not involve any additional hyperparameter, contrary to the GWD and KLW losses. It remains unclear how this is a drawback. What is the concrete effort to tune these hyperparameters ? Does it exist default values for them, and what is the impact on the final performance (best-tuned vs default) ? In brief, are these hyperparameters a real issue ?

Straight arrows from Figure 1 are hard to understand. Why not plotting the smooth l1 loss directly on the graph ?

The authors will make the code public for reproducibility.
summary_of_the_review: The KFIoU loss proposed by the authors seems interesting compared to other SkewIoU estimators, while not relying on additional hyperparameters. While the approach reaches SOTA results on the DOTA dataset, some choices are questionable regarding the other comparisons, making it difficult to compare with the state of the art on the other 2D datasets.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: In this paper, the authors introduce a KFIoU loss for rotated object detection.
The general idea of KFIoU loss is to approximate the SkewIoU based on Gaussian Modeling and Kalman filter. 
Particularly, a bounding box is firstly represented as a Gaussian distribution form.
Then (1) a scale-insensitive center point loss to measure the distance between the centers of Gaussians and (2) a Kalman-based term to measure the overlapping between Gaussian distributions are adopted.
Finally, these measurements are converted back to bounding box form for approximating the SkewIoU.
The proposed approach is validated on several detection benchmarks in both 2D and 3D cases to illustrate its advantages.
strength_and_weaknesses: STRENGTH
- The adoptation of Kalman filter formulation to compute the overlapping in rotated objects is interesting.
- The contributions and roles of each building block are analyzed and discussed. 
- Ablation study and comparison are comprehensive.
- Experiments on both 2D/3D data have shown the advantages of the proposed approach.

WEAKNESS
In Table 2, only L1 smooth is used as baseline.
While the proposed approach give comparable/marginal results with KLD, more comparison with KLD in ablation study is recommended.
clarity,_quality,_novelty_and_reproducibility: Although many building blocks of the proposed approach already exist in previous works.
The paper is well-motivated and the writing is clear. Ablation study and comparison are comprehensive.

summary_of_the_review: Although many building blocks of the proposed approach already exist in previous works.
All modifications and contributions have been discusses. 
Ablation study and comparison are comprehensive.
Experiments have shown the advantages of the proposed approaches.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper introduces the KFIoU loss for rotated object detection. KFIoU approximates the Skew Intersection over Union Loss (SkewIoU) with a Gaussian modelling, making the loss differentiable, and is hyper-parameters free compared to recent works from the literature, e.g. GWD and KLD. KFIoU also includes a center point loss to take into account distance between rotated bounding boxes. Experiments are conducted on aerial images, faces, scene text datasets and autonomous driving benchmarks (KITTI).
The paper initially received mixed reviews, with one clear acceptance recommendation, two borderline accept, one borderline reject, and one reject recommendation. The main concerns pointed out by reviewers related to the motivation of the approach, its positioning with respect to KLD/GWD, its over-claimed connection to Kalman filtering, experimental validation (significance of the results, comparison between KFIoU limited to DOTA). 
The rebuttal and paper's update did a good job in answering reviewers' concerns by adding new conclusive experiments and clarifying the paper's motivations. After discussions, the was a consensus among reviewers that the paper should be accepted. 

The AC carefully reads the submission. The AC considers that the approach is incremental yet interesting and that the last version of the paper is clearly written. Although the justification for not directly optimizing over SkewIoU might be questionable (since the original loss is non-differentiable only in corner case situations), the proposed smooth surrogate loss is sound, hyper-parameter free and thus easy to tune. The experiments are thorough and have been conducted on 2D but also on 3D benchmarks, showing small but consistent gains of KFIoU against the latest baselines. 
Therefore, the AC recommends paper acceptance.
justification_for_why_not_higher_score: - The original SkewIoU loss is non-differentiable only in corner case situations 
- Limited novelty with respect to existing works on Gaussian modelling (GWD and KLD)
- Small experimental gains (although consistent)
justification_for_why_not_lower_score: - Sound approach and paper clearly presented. The approach can be a reference baseline for rotated object detection
- Hyper-parameter free and thus easy to tune approach
- Huge and thorough experimental evaluation, in 2D and 3D, with consistent gains
Decision: Accept: poster