Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper focuses on domain generalization for object detection, especially for the application of autonomous driving. Authors claim that features from images in different domains have different statistics, thus propose to perturb the feature statistics during the training. Experiments are conducted on several autonomous driving datasets with different weather, time of day and synthetic/real sources.
strength_and_weaknesses: ### Strength

* The paper focuses on object detection (and segmentation as well) which is a more practical and more difficult vision task comparing with classification.
* The proposed method does not require extra diversity in the training set or a combination of multiple training sets.
* The performance is pretty good on multiple datasets, and under multiple settings.

### Weaknesses
* Authors claim that Mixstyle and DSU do not work well since the detection training dataset does not have enough variance by default. But it maybe worth trying to add image augmentation to increase the variance of the training data.
* The proposed method seems to be restricted to autonomous driving datasets. Although they may have different style (e.g. weather/time of day) across different domains, the variance between them are still small. It's not clear how the proposed method works on some more complicated variances like object rotation, indoor/outdoor context switch.
clarity,_quality,_novelty_and_reproducibility: The clarity and quality is good. Some extra explanations would be appreciated, for example how are $\Sigma_{\mu}(x)$ and $\Sigma_{\sigma}(x)$ computed in Table 2 and how are "Jigsaw" implemented in Table 5.

The details are enough to reproduce the work.
summary_of_the_review: To summarize, the scope of the application is limited to a single setting (most evaluations are done in autonomous driving datasets), and it's not thoroughly investigated that if more straightforward data augmentation can achieve similar robustness.

----
Thanks for authors' response and extra experiments. I change my rating to weak accept as my concerns have been addressed.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper proposes a simple yet effective normalization perturbation method to synthesize various latent styles during model training so that model can perceive diverse potential domains and generalizes well even without observations of target domain data. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method for object detection in real-world domain shift.
strength_and_weaknesses: Strength
1. The domain adaption for object detection is very important research topic in real-world applications.
2. The motivation of the proposed method is clearly introduced and explained.
3. The proposed method is simple yet effective, demonstrated by extensive experiments.

Weakness
1. The authors claim that “shallow CNN layers which preserve more style information”. Are there any evidences to support this statement ?
2. How to make sure the proposed normalization perturbation can preserve it would not change the image content?

clarity,_quality,_novelty_and_reproducibility: The paper is well written and incremental novelty in method.
summary_of_the_review: The motivation of the paper is with insight, the proposed method is simple yet effective but lack novelty.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper explored the style-sensitive channels for effective style synthesis and proposed Normalization Perturbation for robust object detection. Their method generalizes well under some OOD detection datasets, like Foggy Cityscapes.
strength_and_weaknesses: Strength
- The proposed Normalization Perturbation (NP) method could synthesize various new domain styles in an extremely simple way. 
- The proposed methods cover various potential unseen domain styles in real-world environments, e.g., nighttime and foggy. Enables training models on cityscape to generalize well on Foggy Cityscapes and BDD 100K.

Weakness
- I tend to think that the way of increasing model robustness proposed in this paper is not designed for the object detection task. This is because the approach is a style transformation of image-level features, but ignores box-level features. 
- The classwise mAP is not listed in the paper, so it is not clear whether NP could enhance detectors to be robust to all categories.
- I question the value of this style synthesize method NP for application in object detectors, whether it can be added to a model that already has strong augment and still have large gains, for example, whether it can be added to the current SOTA detectors with strong augment, like dino or Yolo v6.
- Whether this approach will interfere with other data enhancement methods designed for detection tasks, such as InstaBoost. What ratio of NP should be added, if feasible?


clarity,_quality,_novelty_and_reproducibility: See Strength And Weaknesses
summary_of_the_review: See Strength And Weaknesses
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: In this paper, a method called Normalization Perturbation (NP) is proposed for robust object detection under domain shift. The proposed method perturbs the channel statistics of source domain features to synthesize various latent styles. Extensive analysis
and experiments verify the effectiveness of the proposed NP method.

strength_and_weaknesses: Strength: 

(1) The proposed method is simple but effective, demonstrating its power in several tasks including robust object detection, UDA object detection, and DG image classification.

(2) The proposed method is easy to implement, and relies on only one source domain.

(3) The paper is well organized and clearly presented.

Weaknesses:

(1) For domain generalization, the combination of IN and BN layers has also been proved to be simple and effective, such as the IBN network and BIN method. It would be better to also discuss and compare such kind of methods.

(2) While traditional data augmentation methods can be considered as perturbations on image level, there lacks a discussion and comparison on why feature-level perturbation should be better than image-level augmentation to enlarge the diversity of the training data.

(3) It is not very convincing that object detection is hard to be trained with multiple source domains, as even in public domains there are already several datasets with different styles. While it is an advantage that the proposed method only relies on one single source domain for training, it would still be interesting to see experimental comparisons with all methods trained on multiple domains to see the difference.

(4) There are a few other generalizable object detection studies which are not reviewed in the paper, such as:

Lin et al. Domain-Invariant Disentangled Network for Generalizable Object Detection, ICCV 2021.

Hasan et al. Generalizable pedestrian detection: The elephant in the room, CVPR 2021.

clarity,_quality,_novelty_and_reproducibility: Quality: Good.

Clarity: Good.

Novelty: Good.

Reproducibility: Good.
summary_of_the_review: The paper proposes a simple but effective method for robust object detection. To me this work is novel and deserves further attention of the community.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: Paper Summary:
Authors present a method of injecting random noise into feature channel statistics to improve domain generalization of object detectors. It does not require any new or diverse training data, is simply to implement, and yields significantly improved results over baselines.


Review Summary:

Pros:

- Important research topic (ZMVE)
- Clear motivation (ZMVE)
- Well written (kYbi)
- Focused on object detection which is more practical and difficult than classification (QrJD)
- Does not require extra diverse data (QrJD)
- Good performance in variety of settings (QrJD, ZMVE,9C5D,kYbi)
- Simple method (9C5D, kYbi)

Cons:
- Authors claim prior approaches do not work well. Perhaps authors can add data augmention (QrJD) -- authors have complied.
- What about datasets other than driving? (QrJD) 
- Any evidence to support statement that style is in early CNN layers (ZMVE) -- authors have cited prior works
- How to ensure image content is not changed (ZMVE) -- authors have clarified statements. Image content may change, but the belief is not by much. Experimental evidence supports.
- Box level features ignored, shouldn’t be restricted to object detection only (9C5D) -- authors agree method generalizes to other settings and included experiments in appendix.
- Classwise mAP missing (9C5D) -- authors added
- Some missing references (kYbi) -- authors added
- Image perturbation / augmentation not compared (kYbi) -- authors added experiments to address.

AC Recommendation: Accept. Reviewers all lean to accept. 
justification_for_why_not_higher_score: Novelty seems a bit low.
justification_for_why_not_lower_score: Simple but effective solution -- valuable to community.
Decision: Accept: poster