Papers › Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection

Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection

14 May 2019CVPR 2019 6arXiv:1905.05396archive 2025-07-28

Taekyung Kim, Minki Jeong, Seunghyeon Kim, Seokeon Choi, Changick Kim

We introduce a novel unsupervised domain adaptation approach for object detection. We aim to alleviate the imperfect translation problem of pixel-level adaptations, and the source-biased discriminativity problem of feature-level adaptations simultaneously. Our approach is composed of two stages, i.e., Domain Diversification (DD) and Multi-domain-invariant Representation Learning (MRL). At the DD stage, we diversify the distribution of the labeled data by generating various distinctive shifted domains from the source domain. At the MRL stage, we apply adversarial learning with a multi-domain discriminator to encourage feature to be indistinguishable among the domains. DD addresses the source-biased discriminativity, while MRL mitigates the imperfect image translation. We construct a structured domain adaptation framework for our learning paradigm and introduce a practical way of DD for implementation. Our method outperforms the state-of-the-art methods by a large margin of 3%~11% in terms of mean average precision (mAP) on various datasets.

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Tasks

Domain Adaptation Object Detection Representation Learning Translation Unsupervised Domain Adaptation object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image-to-Image Translation	Cityscapes-to-Foggy Cityscapes	Diversify & Match	mAP	34.6	#5 of 6	Archive leaderboard	report
Unsupervised Domain Adaptation	Cityscapes to Foggy Cityscapes	DDMRL	mAP@0.5	34.6	#20 of 22	Archive leaderboard	report

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