Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: Paper addresses the problem of cross-domain object detection. The formed source domain detectors (in the form of Faster R-CNN), learned in a supervised manner, are adopted to perform well on the target domain where no annotations are available. The key to the approach is separate adaptation of the classification head and the regression head within Faster RCNN. This leads to the proposed D-adapt, namely Decoupled Adaptation, that decouples the adversarial adaptation and the training of the detector. In addition, classification and detection heads are adopted in tandem. Experiments show that the proposed D-adapt strategy achieves state-of-the-art results on four cross-domain object detection tasks.
main_review: > Pros:
- The approach is relatively simple and easy to understand
- Performance of the proposed approach is strong, improving on the state-of-the-art

> Cons:
- Despite relative simplicity of the approach, writing obfuscates some of the details, making it more difficult to understand than necessary. Some improvements in exposition, particularly in Section 3, would improve overall readability.
- Technical novelty of the proposed approach is relatively minor. Both adversarial feature adaptation and adaptive refinement of Faster RCNN heads are explored techniques. While the specific strategy proposed in the paper for doing so is novel, the core leveraged technical fundamentals are not. The novelty appears mostly in the use of proposal weighting within adaptation and sequential, rather then parallel, adaptation procedure for the two heads.
- Paper overclaims its contributions. Specifically, it is claimed that "previous methods mainly explored the category adaptation and ignored the regression adaptation" (also Claim 2 of the contributions). This isn't really true. While I am not an expert on cross-domain object detection, a quick search reveals that regression adaptation has been utilized in other past works, e.g., for example in [23] see "Bounding Box Refinement", the pseudo-labeling approaches (e.g., "Multi-Source Domain Adaptation for Object Detection", Yao et al, ICCV) also refine classification and detection. Hence, this claim is at least miss-characterized, needs to be refined and overall contributions clarified.
- For completeness, related work should also discuss approaches for object detection adaptation in semi-supervised learning (not specifically designed for cross-domain object detection, but leveraging techniques relevant to the approach). These include: (1) "Note-rcnn: Noise tolerant ensemble rcnn for semi- supervised object detection", Gao et al, ICCV, 2019 and (2) UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation, Khandelwal et al, CVPR 2021, among others.
- Ablations show significance of the proposal weighting in the adaptation (good!), as well as significance of adaptation of regression for bounding boxes (also good!), however, fail to illustrate that decoupling and sequential nature of adaptors (Figure 1d) is important. Given that this is one of the core contributions, addition of such ablation would be very useful. 
- Minor: It is unclear why certain approaches were cited in related work, but do not appear to be compared against in the tables (e.g., [57]) 
summary_of_the_review: Overall, the approach is sounds and experimentally performs well. However, the novelty and significance are somewhat limited. Further, exposition can be improved and claims (specifically claim 2) are (at least) imprecise and miss-characterize prior work. Finally, some ablations that would have been useful are missing and should be included. Given these concerns, overall, I view the paper as being slightly below bar for acceptance. I would be happy to revisit my assessment, and potentially increase the score, if authors can convincingly address the raised concerns in the rebuttal. 

Authors have addressed a number of my comments in the rebuttal, and promised to addressed others (claims) in the camera ready revision. As such, I am raising my score and believe the paper should be accepted.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper focus on object detection domain adaptation to transfer a detector from a source domain, in the context where sufficient training data is available on a source domain but only unlabeled data is available in the target domain. It presents good results adapting from/to Pascal, Clipart, Comic, Sim10k, Cityscapes and FoggyCityscapes.

main_review: The quality of the introduction is inferior to the rest of the paper, thus it can be improved.
- As the paper presents the problem, it poses a "Data challenge: what to adapt in the adversarial training is unknown." It is not clear at this point of the paper what is the relation between domain adaptation and adversarial training. For clarity, the connection that currently is made  on "Related Work" should be presented as part of the motivation presented in the introduction to situate the reader on the challenges presented.
- The text poses "Architecture challenge" but it is also not clear on the following text, that should describe the challenge, how the argument is any related to architecture. The arguments presented in the text that follows (again without much context at this point) is that they are related to training policy and regularization, but not architecture specifically. Similar to the first challenge presented, the text is lacking a better context and justification.
- The "loss challenge" will only be clear from section 3.1 on, as the introduction text defining it is not very informative. 

For the sake of completeness and clarity, the main concepts related to domain adaptation and adversarial training, such as adaptors for domain adaptation, adversarial alignment and adversarial regressor, must be defined and referenced before its use and/or reformulated in the context of the paper. 

On results comparison, it would be important to state any architecture difference (or state otherwise) between the proposal and compared approaches. Do they use same detector architecture?

It is also important to clarify what is the oracle used (and better justify its errors from Figure 5). 
In that direction, the text should also clarify the results presented that surpassed the oracle results. This happens on Table 2 for "bike" and "car", and on Table 4, it is not clear how performance on "rider" and "bicycle" surpassed the oracle results for VGG backbone and "truck", "motorcycle" and "bicycle" for Resnet101. 

Overall, the experimental results are compelling, but it is necessary to better link the concepts used with pre-existent approaches on adversarial domain adaptation.
summary_of_the_review: The paper presents a clever combination of domain adversarial training for adapting the components of object detection. The experimental results obtain SOTA for some combinations explored. The weakest part is of the presentation is the introduction, and should be reviewed. Also,  although the set of references included have a good cover of the area, the concepts presented in the text are not connected to pre-existent concepts as it would be appropriate to be.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The authors propose a tailored method, named Decoupled Adaptation (D-adapt), for cross-domain object detection. The conventional domain adaptation techniques hurt the discriminability of the detector and ignores the adaptation on bounding box regression. In order to avoid hurting the discriminability of the detector, the authors propose to decouple the adaptation modules from the detector, which achieves the parameter independence. In addition, the authors propose the module for adapting the bounding box regression. Due to the decoupling property, the proposed D-adapt framework is compatible to many off-the-shelf detectors. The experimental results are very promising.
main_review: Strengths:
1. The paper is very well-written.
2. The proposed idea is simple and intuitive, but turns out to be quite effective.
3. The decoupled adaption is a great option for avoiding the hurt to the detector's discriminability.
4. The decoupled adaption design enables the ease of incorporating the box regression adaption, which is novel.
5. The experiments are extensive and persuasive.

Weaknesses:
Will the authors release their code? They do not mention it at all.
summary_of_the_review: Considering the simplicity, novelty and effectiveness, this paper is of high quality and above the acceptance bar.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This work provides a systematic way to adapt object detectors to an unseen target domain. Specifically, the object classification and bounding box regression heads are adapted one after the other, and ROIs are used to train a separate set of adaptors to provide foreground feature alignment without hurting the discriminablity of the detectors. 
main_review: Positives:
+ The paper is well written and the problem is well motivated.  
+ The experiments on the standard benchmarks are reported and comparisons are provided with recent works.
+ The ablation study is formulated to reflect the claims.

Clarifications needed:
- the bounding box adaptors take as input the foreground proposals, which are the crops of objects, as shown in figure 3. How does the regressor predict offsets if it has been given a close crop of the objects? The claim made is that the confirmation bias of the pseudo labels with the use of the adaptors. If the adaptors are trained to align the features such that the discriminator is confused, which means the source bounding box distribution needs to be matched with the target, hence biasing the regressor again.
-T is set to be 3, is saturation seen after a point. Would different domain pairs have different saturation iteration? How was this value chosen? Shouldn't it be fair to also report mAP scores for T=1, in Table 3 and 4 .  
-Table 1 is missing UMT[10] baseline which reports mAP 44.1 for VOC to Clipart which is a stronger baseline for the pseudo-label based techniques. 
-Table2 doesn't mention the backbone used in the experiment
- The error analysis part should also include the bounding box adaptor part to highlight how much bias is removed using bounding box adaptor.
- the line on page 9: "The gain brought by box adaptation is consistent, for example when T = 3, it can still improve the mAP from 47.0 to 49.1." should it be 45.0 instead of 47.0?
summary_of_the_review: The approach highlights the shortcomings of the previous methods and systematically tries to resolve them. There are a few issues with the comparisons and clarification is needed for one of the claims.  Overall the writing and structure of the paper is good.
review_title: null

Meta-review: This paper decouples the adversarial training of a domain adaptation model with the detector learning process, and is able to disentangle the features of foreground and background when performing adaptation.  State of the art results on four different domains/tasks are presented with significant improvement. Reviewers are unanimous that the submission is acceptable.  Reviewer PJVS is the most authoritative and experienced reviewer, and notes the paper clarity is impaired, and that the paper is immodest in various places and overclaims what is otherwise supported by the results therein.  The AC concurs with this view.  However the paper is acceptable in present form for ICLR, and the AC advises the authors to revise as discussed in the rebuttal and response to the rebuttal.
Decision: Accept (Poster)