Papers › AWADA: Attention-Weighted Adversarial Domain Adaptation for Object Detection

AWADA: Attention-Weighted Adversarial Domain Adaptation for Object Detection

31 Aug 2022arXiv:2208.14662archive 2025-07-28

Maximilian Menke, Thomas Wenzel, Andreas Schwung

Object detection networks have reached an impressive performance level, yet a lack of suitable data in specific applications often limits it in practice. Typically, additional data sources are utilized to support the training task. In these, however, domain gaps between different data sources pose a challenge in deep learning. GAN-based image-to-image style-transfer is commonly applied to shrink the domain gap, but is unstable and decoupled from the object detection task. We propose AWADA, an Attention-Weighted Adversarial Domain Adaptation framework for creating a feedback loop between style-transformation and detection task. By constructing foreground object attention maps from object detector proposals, we focus the transformation on foreground object regions and stabilize style-transfer training. In extensive experiments and ablation studies, we show that AWADA reaches state-of-the-art unsupervised domain adaptation object detection performance in the commonly used benchmarks for tasks such as synthetic-to-real, adverse weather and cross-camera adaptation.

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Tasks

Domain Adaptation Object Object Detection Style Transfer Unsupervised Domain Adaptation object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Unsupervised Domain Adaptation	BDD100k to Cityscapes	AWADA	mAP	31.5	#3 of 4	Archive leaderboard	report
Unsupervised Domain Adaptation	Cityscapes to Foggy Cityscapes	AWADA	mAP@0.5	44.8	#10 of 22	Archive leaderboard	report
Unsupervised Domain Adaptation	SIM10K to Cityscapes	AWADA	mAP@0.5	54.1	#11 of 13	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.

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