Open-Set Domain Adaptation with Visual-Language Foundation Models

• URL: http://arxiv.org/abs/2307.16204v1
• Date: Sun, 30 Jul 2023 11:38:46 GMT
• Title: Open-Set Domain Adaptation with Visual-Language Foundation Models
• Authors: Qing Yu and Go Irie and Kiyoharu Aizawa
• Abstract summary: Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge from a source domain to a target domain with unlabeled data. Open-set domain adaptation (ODA) has emerged as a potential solution to identify these classes during the training phase.
• Score: 51.49854335102149
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge obtained from a source domain with labeled data to a target domain with unlabeled data. Owing to the lack of labeled data in the target domain and the possible presence of unknown classes, open-set domain adaptation (ODA) has emerged as a potential solution to identify these classes during the training phase. Although existing ODA approaches aim to solve the distribution shifts between the source and target domains, most methods fine-tuned ImageNet pre-trained models on the source domain with the adaptation on the target domain. Recent visual-language foundation models (VLFM), such as Contrastive Language-Image Pre-Training (CLIP), are robust to many distribution shifts and, therefore, should substantially improve the performance of ODA. In this work, we explore generic ways to adopt CLIP, a popular VLFM, for ODA. We investigate the performance of zero-shot prediction using CLIP, and then propose an entropy optimization strategy to assist the ODA models with the outputs of CLIP. The proposed approach achieves state-of-the-art results on various benchmarks, demonstrating its effectiveness in addressing the ODA problem.

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