SAMB-TransformerUDA

Semantic-adaptive Message Broadcasting for Transformer-based UDA

Xin Li, Cuiling Lan, Guoqiang Wei, Zhibo Chen

University of Science and Technology of China (USTC), Microsoft Research Asia (MSRA),

📌 News!!!

• [x] | 07/09/2023 | The code for the first version of training strategies was released.
• [ ] The second version of the training strategy will be updated with better performance.

🌟 Motivation

Usage

Please replace the base_trainer.py in the framework of ToAlign with this file and the model part with your model.

Cite US

Please cite us if this work is helpful to you.

@article{li2022semantic,
  title={Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation},
  author={Li, Xin and Lan, Cuiling and Wei, Guoqiang and Chen, Zhibo},
  journal={arXiv preprint arXiv:2212.02739},
  year={2022}
}

Abstract

Vision transformer has demonstrated great potential in abundant vision tasks. However, it also inevitably suffers from poor generalization capability when the distribution shift occurs in testing (i.e., out-of-distribution data). To mitigate this issue, we propose a novel method, Semantic-aware Message Broadcasting (SAMB), which enables more informative and flexible feature alignment for unsupervised domain adaptation (UDA). Particularly, we study the attention module in the vision transformer and notice that the alignment space using one global class token lacks enough flexibility, where it interacts information with all image tokens in the same manner but ignores the rich semantics of different regions. In this paper, we aim to improve the richness of the alignment features by enabling semanticaware adaptive message broadcasting. Particularly, we introduce a group of learned group tokens as nodes to aggregate the global information from all image tokens, but encourage different group tokens to adaptively focus on the message broadcasting to different semantic regions. In this way, our message broadcasting encourages the group tokens to learn more informative and diverse information for effective domain alignment. Moreover, we systematically study the effects of adversarial-based feature alignment (ADA) and pseudo-label based self-training (PST) on UDA. We find that one simple two-stage training strategy with the cooperation of ADA and PST can further improve the adaptation capability of the vision transformer. Extensive experiments on DomainNet, OfficeHome, and VisDA-2017 demonstrate the effectiveness of our methods for UDA

Acknowledgments

• ToAlign
• CDTrans