Rethinking Domain Adaptation and Generalization in the Era of CLIP
<|reference_start|>Rethinking Domain Adaptation and Generalization in the Era of CLIP: In recent studies on domain adaptation, significant emphasis has been placed on the advancement of learning shared knowledge from a source domain to a target domain. Recently, the large vision-language pre-trained model, i.e., CLIP has shown strong ability on zero-shot recognition, and parameter efficient tuning can further improve its performance on specific tasks. This work demonstrates that a simple domain prior boosts CLIP's zero-shot recognition in a specific domain. Besides, CLIP's adaptation relies less on source domain data due to its diverse pre-training dataset. Furthermore, we create a benchmark for zero-shot adaptation and pseudo-labeling based self-training with CLIP. Last but not least, we propose to improve the task generalization ability of CLIP from multiple unlabeled domains, which is a more practical and unique scenario. We believe our findings motivate a rethinking of domain adaptation benchmarks and the associated role of related algorithms in the era of CLIP.<|reference_end|>
@article{feng2024rethinking,
    title={Rethinking Domain Adaptation and Generalization in the Era of CLIP},
    author={Ruoyu Feng, Tao Yu, Xin Jin, Xiaoyuan Yu, Lei Xiao, Zhibo Chen},
    journal={arXiv preprint arXiv:2407.15173},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15173},
    primaryClass={cs.CV}
}