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

arXiv: https://arxiv.org/abs/2309.09496 · HTML: https://mlsanity.com/paper/2309.09496/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 21 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [Person Search with Natural Language Description](https://mlsanity.com/paper/1702.05729/) (2017)
- [Dual-Path Convolutional Image-Text Embeddings with Instance Loss](https://mlsanity.com/paper/1711.05535/) (2017)
- [Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval](https://mlsanity.com/paper/2303.12501/) (2023)
- [Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification](https://mlsanity.com/paper/2107.12666/) (2021)
- [DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval](https://mlsanity.com/paper/2109.05534/) (2021)
- [CLIP-Driven Fine-grained Text-Image Person Re-identification](https://mlsanity.com/paper/2210.10276/) (2022)
- [ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language](https://mlsanity.com/paper/2005.07327/) (2020)
- [See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval](https://mlsanity.com/paper/2208.08608/) (2022)
- [Learning Granularity-Unified Representations for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2207.07802/) (2022)
- [TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search](https://mlsanity.com/paper/2105.11628/) (2021)
- [Text-Based Person Search with Limited Data](https://mlsanity.com/paper/2110.10807/) (2021)
- [Learning Semantic-Aligned Feature Representation for Text-based Person Search](https://mlsanity.com/paper/2112.06714/) (2021)
- [AXM-Net: Implicit Cross-Modal Feature Alignment for Person Re-identification](https://mlsanity.com/paper/2101.08238/) (2021)

## Built on by (most cited first, 10 of 10)

- [IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2503.10324/) (2025)
- [MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt](https://mlsanity.com/paper/2412.10707/) (2024)
- [DM-Adapter: Domain-Aware Mixture-of-Adapters for Text-Based Person Retrieval](https://mlsanity.com/paper/2503.04144/) (2025)
- [UP-Person: Unified Parameter-Efficient Transfer Learning for Text-based Person Retrieval](https://mlsanity.com/paper/2504.10084/) (2025)
- [Enhancing Visual Representation for Text-based Person Searching](https://mlsanity.com/paper/2412.20646/) (2024)
- [FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification](https://mlsanity.com/paper/2510.15595/) (2025)
- [ITSELF: Attention Guided Fine-Grained Alignment for Vision-Language Retrieval](https://mlsanity.com/paper/2601.01024/) (2026)
- [AMNS: Attention-Weighted Selective Mask and Noise Label Suppression for Text-to-Image Person Retrieval](https://mlsanity.com/paper/2409.06385/) (2024)
- [Unleashing MLLMs on the Edge: A Unified Framework for Cross-Modal ReID via Adaptive SVD Distillation](https://mlsanity.com/paper/2602.12936/) (2026)
- [Achieving Text-based Person Retrieval with Any Granularity](https://mlsanity.com/paper/2607.21057/) (2026)

## Abstract (arXiv)

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.