Vision-aware Multimodal Prompt Tuning for Uploadable Multi-source Few-shot Domain Adaptation

• URL: http://arxiv.org/abs/2503.06106v1
• Date: Sat, 08 Mar 2025 07:17:06 GMT
• Title: Vision-aware Multimodal Prompt Tuning for Uploadable Multi-source Few-shot Domain Adaptation
• Authors: Kuanghong Liu, Jin Wang, Kangjian He, Dan Xu, Xuejie Zhang,
• Abstract summary: This paper introduces an uploadable multi-source few-shot domain adaptation (UMFDA) schema.<n>It belongs to a decentralized edge collaborative learning in the edge-side models that must maintain a low computational load.<n>A vision-aware multimodal prompt tuning framework (VAMP) under the decentralized schema is proposed.
• Score: 12.380114998101433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional multi-source domain few-shot adaptation (MFDA) faces the challenge of further reducing the load on edge-side devices in low-resource scenarios. Considering the native language-supervised advantage of CLIP and the plug-and-play nature of prompt to transfer CLIP efficiently, this paper introduces an uploadable multi-source few-shot domain adaptation (UMFDA) schema. It belongs to a decentralized edge collaborative learning in the edge-side models that must maintain a low computational load. And only a limited amount of annotations in source domain data is provided, with most of the data being unannotated. Further, this paper proposes a vision-aware multimodal prompt tuning framework (VAMP) under the decentralized schema, where the vision-aware prompt guides the text domain-specific prompt to maintain semantic discriminability and perceive the domain information. The cross-modal semantic and domain distribution alignment losses optimize each edge-side model, while text classifier consistency and semantic diversity losses promote collaborative learning among edge-side models. Extensive experiments were conducted on OfficeHome and DomainNet datasets to demonstrate the effectiveness of the proposed VAMP in the UMFDA, which outperformed the previous prompt tuning methods.

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