@inproceedings{
li2024daada,
title={{DA}-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection},
author={Haochen Li and Rui Zhang and Hantao Yao and Xin Zhang and Yifan Hao and Xinkai Song and Xiaqing Li and Yongwei Zhao and Yunji Chen and Ling Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hkEwwAqmCk}
}
Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain.
As the visual-language models (VLMs) can provide essential general knowledge on unseen images, freezing the visual encoder and inserting a domain-agnostic adapter can learn domain-invariant knowledge for DAOD.
However, the domain-agnostic adapter is inevitably biased to the source domain.
It discards some beneficial knowledge discriminative on the unlabelled domain, \ie domain-specific knowledge of the target domain.
To solve the issue, we propose a novel Domain-Aware Adapter (DA-Ada) tailored for the DAOD task.
The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariant knowledge.
DA-Ada consists of the Domain-Invariant Adapter (DIA) for learning domain-invariant knowledge and the Domain-Specific Adapter (DSA) for injecting the domain-specific knowledge from the information discarded by the visual encoder.
Comprehensive experiments over multiple DAOD tasks show that DA-Ada can efficiently infer a domain-aware visual encoder for boosting domain adaptive object detection.
Our code is available at https://github.com/Therock90421/DA-Ada.
DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection
Haochen Li Rui Zhang Hantao Yao Xin Zhang Yifan Hao Xinkai Song Xiaqing Li Yongwei Zhao Yunji Chen Ling Li