Papers › Object-Aware Domain Generalization for Object Detection

Object-Aware Domain Generalization for Object Detection

19 Dec 2023arXiv:2312.12133archive 2025-07-28

Wooju Lee, Dasol Hong, Hyungtae Lim, Hyun Myung

Single-domain generalization (S-DG) aims to generalize a model to unseen environments with a single-source domain. However, most S-DG approaches have been conducted in the field of classification. When these approaches are applied to object detection, the semantic features of some objects can be damaged, which can lead to imprecise object localization and misclassification. To address these problems, we propose an object-aware domain generalization (OA-DG) method for single-domain generalization in object detection. Our method consists of data augmentation and training strategy, which are called OA-Mix and OA-Loss, respectively. OA-Mix generates multi-domain data with multi-level transformation and object-aware mixing strategy. OA-Loss enables models to learn domain-invariant representations for objects and backgrounds from the original and OA-Mixed images. Our proposed method outperforms state-of-the-art works on standard benchmarks. Our code is available at https://github.com/WoojuLee24/OA-DG.

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Code

WoojuLee24/OA-DG officialmentioned in papermentioned on GitHubpytorchGPL-3.0 report

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Tasks

Autonomous Driving Contrastive Learning Data Augmentation Domain Generalization Object Object Detection Object Localization Robust Object Detection object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Robust Object Detection	Cityscapes	OA-DG	mPC [AP]	21.8	#5 of 13	Archive leaderboard	report
Robust Object Detection	Cityscapes	OA-Mix	mPC [AP]	20.8	#7 of 13	Archive leaderboard	report
Robust Object Detection	DWD	OA-DG	mPC [AP50]	31.8	#5 of 12	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: OA-Loss, OA-Mix

OA-Loss OA-Mix

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