DMPT: Decoupled Modality-aware Prompt Tuning for Multi-modal Object Re-identification

• URL: http://arxiv.org/abs/2504.10985v1
• Date: Tue, 15 Apr 2025 08:48:41 GMT
• Title: DMPT: Decoupled Modality-aware Prompt Tuning for Multi-modal Object Re-identification
• Abstract summary: We propose an efficient prompt-tuning framework tailored for multi-modal object re-identification.<n>Our framework freezes the main backbone and only optimize several newly added decoupled modality-aware parameters.<n>We show that our framework can achieve competitive results to existing state-of-the-art methods while requiring only 6.5% fine-tuning of the backbone parameters.
• Score: 28.391851855141976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current multi-modal object re-identification approaches based on large-scale pre-trained backbones (i.e., ViT) have displayed remarkable progress and achieved excellent performance. However, these methods usually adopt the standard full fine-tuning paradigm, which requires the optimization of considerable backbone parameters, causing extensive computational and storage requirements. In this work, we propose an efficient prompt-tuning framework tailored for multi-modal object re-identification, dubbed DMPT, which freezes the main backbone and only optimizes several newly added decoupled modality-aware parameters. Specifically, we explicitly decouple the visual prompts into modality-specific prompts which leverage prior modality knowledge from a powerful text encoder and modality-independent semantic prompts which extract semantic information from multi-modal inputs, such as visible, near-infrared, and thermal-infrared. Built upon the extracted features, we further design a Prompt Inverse Bind (PromptIBind) strategy that employs bind prompts as a medium to connect the semantic prompt tokens of different modalities and facilitates the exchange of complementary multi-modal information, boosting final re-identification results. Experimental results on multiple common benchmarks demonstrate that our DMPT can achieve competitive results to existing state-of-the-art methods while requiring only 6.5% fine-tuning of the backbone parameters.

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