Traditional domain adaptation assumes the same vocabulary across source and target domains, which often struggles with limited transfer flexibility and efficiency while handling target domains with different vocabularies. Inspired by recent vision-language models (VLMs) that enable open-vocabulary visual recognition by reasoning on both images and texts, we study open-vocabulary domain adaptation (OVDA), a new unsupervised domain adaptation framework that positions a pre-trained VLM as the source model and transfers it towards arbitrary unlabelled target domains. To this end, we design a Prompt Ensemble Self-training (PEST) technique that exploits the synergy between vision and language to mitigate the domain discrepancies in image and text distributions simultaneously. Specifically, PEST makes use of the complementary property of multiple prompts within and across vision and language modalities, which enables joint exploitation of vision and language information and effective learning of image-text correspondences in the unlabelled target domains. Additionally, PEST captures temporal information via temporal prompt ensemble which helps memorize previously learnt target information. Extensive experiments show that PEST outperforms the state-of-the-art consistently across 10 image recognition tasks.

本文提出了一种基于VLM的开放词汇无监督领域自适应框架，并通过Prompt Ensemble Self-Training技术来提高领域间的图像和文本分布的转移灵活性和效率， 实验结果表明其在10个图像识别任务中比目前最先进的技术表现更优秀。

基于提示集成的自训练方法用于开放词汇领域自适应