Paper: Context-Aware Unsupervised Domain Adaptive Lane Detection (ICLR-2024)

Reviewer comment (Experiments):
The experimentation settings in the paper are confusing as it is unclear which dataset is used as the source domain and which one is used as the target domain. This lack of clarity hinders proper understanding and evaluation of the proposed approach.

Author response:
We fully agree with the reviewers, and in response to this constructive comment, we have added a detailed description of the datasets used for the dataset source and target domains in Appendix A1.

Reviewer comment (Experiments):
The paper fails to fully explore publicly available datasets from different domains. It lacks experimentation on datasets such as CuLane and OpenLane, which limits the generalizability and thoroughness of the findings.

Author response:
We totally agree with the reviewer. Following this constructive comment, we have supplemented the crossover experiments using different domain datasets to fully demonstrate the generalizability and performance of our method, including the transfer experiments between CULane and OpenLane. The experimental results are shown in Table 4 of Section 4.3 and Table A7 of Appendix A5. DACCA demonstrates superior performance compared to existing methods across all metrics, particularly when transferring from the CULane dataset to the Openlane dataset. It further confirms the generalization capability of our method.

Reviewer comment (Novelty):
The algorithm presented in the paper is only applicable to segmentation-based lane detection methods. This limitation reduces its potential contribution since most of today's algorithms tend to be either transformer-based or keypoint-based, making the proposed approach less relevant to current state-of-the-art techniques.

Author response:
Here we would like to make further clarification. As pointed out in the review, in this work we mainly use a segmentation-based approach to illustrate the whole pipeline of the method, but our method has the potential to be a generalized domain adaptive framework for lane detection. Firstly, the anchor, positive sample, and negative sample in contrastive learning are not limited to pixels, but can be keypoints and queries in a transformer. Secondly, the DFA module is essentially a plug-and-play feature aggregation module. Our future work will validate the effectiveness and advantages of our approach by applying it to other lane line detection methods. We have pointed out it in the conclusion section.

Reviewer comment (Novelty):
Limited originality in cross-domain contrastive learning. This component heavily relies on the previous work (Wang et al., 2021; 2023) and provides an increment improvement (a positive sample selection). It doesn’t strike me as particularly novel.

Author response:
We would like to further explain the originality of our work, and expect further kind and careful evaluation from the reviewer. The existing works (Wang et al., 2021; 2023) mainly focus on improving the form of contrastive loss, while the core of our proposed cross-domain contrastive loss is in the selection of samples without any changes to the contrastive loss. That is, we believe that the selection of samples in contrast learning is equally critical, so we re-select the anchor, positive sample, and negative sample involved in contrastive loss, thereby achieving better results.

Reviewer comment (Presentation):
Combinatorial contribution. Each contribution addresses a specific issue in the unsupervised domain-adaptive field. The entire paper lacks content coherence.

Author response:
As the reviewer will note, two components, cross-domain contrastive loss and domain-level feature aggregation (DFA), of our approach are complementary and indispensable, not simply combined. The former is responsible for aligning features between different domains, and the latter focuses more on discriminative features inter-categories within a domain. The results of the ablation experiments show that the models are underperforming without either component, as shown in Figures A3 and A4. We sincerely hope the reviewers to give further consideration and re-evaluation to our approach.

Reviewer comment (Presentation):
The title fails to effectively represent the contributions of the paper. 'Context' is a broad concept. Here, the authors primarily propose aggregating domain-level context for pixel-level feature enhancement. Therefore, it is best to highlight this aspect.

Author response:
We appreciate the keen insight of the reviewer, and have revised the title of the paper to “Unsupervised Domain Adaptive Lane Detection via Contextual Contrast and Aggregation”, which we believe can highlight the contributions of this work.

Reviewer comment (Writing):
The quality of writing and diagrams in the paper requires improvement. Several definitions provided in the article lack clarity, hindering reader comprehension. For instance, in section 3.2, the term 'anchor' is introduced without a precise definition. Additionally, the role of modules is not adequately explained, such as how the introduction of PSMM ensures the appropriate assignment of positive samples. Furthermore, the figures lack detail and refinement, and the overall layout, as seen in Figure 2, does not appear to be carefully designed.

Author response:
As the reviewer will note, we have done our best to improve the quality of the writing and images in the revision, including pointing out that the anchor is the pixel feature being compared, i.e., the candidate pixel, the feature representation in PSMM is more accurate than pixel-level feature representation, providing more positive signals in the contrastive loss, as well as the improvement of the appearance of each module in the Student-Teacher model structure, as shown in Figure 2.

Reviewer comment (Experiments):
Some conclusions of the article are exaggerated. For example, in section 4.3, the authors claim that their method performs significantly better than other methods when using ERFNet as the detection model, but choosing PyCDA (Accuracy/%: 86.73) and MLDA(Accuracy/%: 88.43) to compare instead of SGPCS with the accuracy of 89.28%.

Author response:
We totally agree with the reviewer. We have revised and clarified the inaccurate expressions in this manuscript.

Reviewer comment (Novelty):
There’s limited discussion, comparison and analysis over prior work that also target specifically on laneline domain adaption, for instance, MLDA (Li et al., 2022). The statement in introduction that 'Unfortunately, achieving cross-domain context dependency in domain-adaptive lane detection remains unexplored.' is also less accurate as prior works like MLDA already explored this domain.

Author response:
Following the reviewer's comment, we have added a review of methods such as MLDA in the introduction section and supplemented a comparison and discussion with MLDA in the experimental section (Sect. 4.3). At the same time, we have revised and clarified the inaccuracies in Introduction.

Reviewer comment (Experiments):
Author only conduct experiment on TuLane, MuLane, and MoLane, three simulated lane datasets proposed from one single work. The result would be more convincing if authors could conduct domain transfer experiments between Tusimple and Culane, which are more commonly used dataset for the community.

Author response:
Following this constructive comment. In this revision, we have supplemented the domain transfer experiments between CULane and Tusimple, detailed in Appendix A.5.

Reviewer comment (Writing):
This work is a bit hard to follow, for instance, in 3.3, author use ‘features from a whole domain is more beneficial’ as a core argument over prior work, without clearly pointing to ‘what is whole domain and how does the method align with whole domain’ in the following implementation. The implementation is also hard to follow without clearly define ‘lane feature and domain feature’ in the first place.

Author response:
As the reviewer will note, we have made our best to clarify and explain some confusing concepts and terms. Specifically, we carefully define lane features and domain features in this revision. The former refers to pixel-level features of a lane, while the latter refers to all lane-line pixel features in the source and target domains. In addition, whole domain features refer to the pixel-level features of lanes in all images in a domain. Since DFA directly fuses domain features into individual lane line pixel features, it enables the model to learn domain-level features better and thus better align with the whole domain.

Reviewer comment (Writing):
The conclusion of ‘with more advanced lane detection methods, e.g., anchor-based methods’ is also lack of context. Firstly, in the related work, author stated that ‘In this paper, we consider segmentation-based domain-adaptive lane detection.’, which contradict with the conclusion. Secondly, author hasn’t provide context why ‘author based’ method is superiority over other method’.

Author response:
As pointed out in this review comment, the expression in our conclusions was inappropriate and has been revised. We plan to apply our framework to other types of lane detection methods, e.g., anchor-based, and transformer-based approaches, to verify its generality in the future.

Reviewer comment (Writing):
Author mentioned the term PSMMs multiple times in the introduction and related work, without explicitly explaining its functionality. It would be easier to follow if author could briefly explain its functionality earlier in the paper.

Author response:
Following this constructive suggestion, we have added to Introduction a description of the functionality of PSMMs, i.e., saving domain features of each line.

Reviewer comment (Presentation):
Please also include proper citation of work for table 4/5, and the last two page of the paper.

Author response:
We have cited proper work for Tables 4, A8, and A9 in the revision.

Reviewer comment (Writing):
It is still less clear what the motivation and implementation of cross-domain contrastive loss is. It seems like the only change author proposed is a novel sampling method, please make this clear.

Author response:
The motivation for proposing cross-domain contrastive loss is to achieve high effectiveness in learning discriminative features. As demonstrated in Figure A4, cross-domain contrastive loss has solved the problem that cross-domain feature dependency only aligns inter-domain features but does not discriminate well inter-category features without modifying the loss function. We have proposed a new sampling policy to implement the cross-domain contrastive loss. In contrastive learning, sample selection is very important, while existing works in (Wang et al., 2021, 2023), only consider positive sample selection but disregard the selection of anchor and negative samples.