CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval

• URL: http://arxiv.org/abs/2309.09496v2
• Date: Tue, 2 Jan 2024 05:01:26 GMT
• Title: CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval
• Authors: Yating Liu, Yaowei Li, Zimo Liu, Wenming Yang, Yaowei Wang, Qingmin Liao
• Abstract summary: We introduce a CLIP-based Synergistic Knowledge Transfer (CSKT) approach for Person Retrieval (TPR) To explore the CLIP's knowledge on input side, we first propose a Bidirectional Prompts Transferring (BPT) module constructed by text-to-image and image-to-text bidirectional prompts and coupling projections. CSKT outperforms the state-of-the-art approaches across three benchmark datasets when the training parameters merely account for 7.4% of the entire model.
• Score: 66.93563107820687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-based Person Retrieval (TPR) aims to retrieve the target person images given a textual query. The primary challenge lies in bridging the substantial gap between vision and language modalities, especially when dealing with limited large-scale datasets. In this paper, we introduce a CLIP-based Synergistic Knowledge Transfer (CSKT) approach for TPR. Specifically, to explore the CLIP's knowledge on input side, we first propose a Bidirectional Prompts Transferring (BPT) module constructed by text-to-image and image-to-text bidirectional prompts and coupling projections. Secondly, Dual Adapters Transferring (DAT) is designed to transfer knowledge on output side of Multi-Head Attention (MHA) in vision and language. This synergistic two-way collaborative mechanism promotes the early-stage feature fusion and efficiently exploits the existing knowledge of CLIP. CSKT outperforms the state-of-the-art approaches across three benchmark datasets when the training parameters merely account for 7.4% of the entire model, demonstrating its remarkable efficiency, effectiveness and generalization.

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