MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt

• URL: http://arxiv.org/abs/2412.10707v1
• Date: Sat, 14 Dec 2024 06:33:53 GMT
• Title: MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt
• Authors: Yuhao Wang, Xuehu Liu, Tianyu Yan, Yang Liu, Aihua Zheng, Pingping Zhang, Huchuan Lu,
• Abstract summary: Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities.<n>Recently, large-scale pre-trained models like CLIP have demonstrated impressive performance in traditional single-modal object ReID tasks.<n>We introduce a novel framework called MambaPro for multi-modal object ReID.
• Score: 60.10555128510744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities. Recently, large-scale pre-trained models like CLIP have demonstrated impressive performance in traditional single-modal object ReID tasks. However, they remain unexplored for multi-modal object ReID. Furthermore, current multi-modal aggregation methods have obvious limitations in dealing with long sequences from different modalities. To address above issues, we introduce a novel framework called MambaPro for multi-modal object ReID. To be specific, we first employ a Parallel Feed-Forward Adapter (PFA) for adapting CLIP to multi-modal object ReID. Then, we propose the Synergistic Residual Prompt (SRP) to guide the joint learning of multi-modal features. Finally, leveraging Mamba's superior scalability for long sequences, we introduce Mamba Aggregation (MA) to efficiently model interactions between different modalities. As a result, MambaPro could extract more robust features with lower complexity. Extensive experiments on three multi-modal object ReID benchmarks (i.e., RGBNT201, RGBNT100 and MSVR310) validate the effectiveness of our proposed methods. The source code is available at https://github.com/924973292/MambaPro.

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