  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.
