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<p align="center"> <h1 align="center">MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt</h1> </p> <p align="center"> <img src="results/logo.png" alt="Description of the image" style="width:54%;"> <p align="center"> <p align="center"> <a href="https://scholar.google.com/citations?user=WZvjVLkAAAAJ&hl=zh-CN" rel="external nofollow noopener" target="_blank"><strong>Yuhao Wang</strong></a> · <a href="https://scholar.google.com/citations?user=6R4C_6wAAAAJ&hl=zh-CN&oi=ao" rel="external nofollow noopener" target="_blank"><strong>Xuehu Liu</strong></a> · <a href="https://openreview.net/profile?id=~Tianyu_Yan2" rel="external nofollow noopener" target="_blank"><strong>Tianyu Yan</strong></a> · <a href="https://dblp.org/pid/51/3710-66.html" rel="external nofollow noopener" target="_blank"><strong>Yang Liu</strong></a> · <a href="https://ai.ahu.edu.cn/2022/0407/c19212a283203/page.htm" rel="external nofollow noopener" target="_blank"><strong>Aihua Zheng</strong></a> · <a href="https://scholar.google.com/citations?user=MfbIbuEAAAAJ&hl=zh-CN" rel="external nofollow noopener" target="_blank"><strong>Pingping Zhang*</strong></a> · <a href="#" rel="external nofollow noopener" target="_blank"><strong>Huchuan Lu</strong></a> </p> <p align="center"> <a href="https://arxiv.org/pdf/2412.10707" rel="external nofollow noopener" target="_blank">AAAI 2025 Paper</a> </p> <p align="center"> <img src="results/Overall.png" alt="Overall Framework" style="width:100%;"> </p>

MambaPro is a novel multi-modal object ReID framework that integrates CLIP's pre-trained capabilities with state-of-the-art multi-modal aggregation techniques. Using Parallel Feed-Forward Adapters (PFA), Synergistic Residual Prompts (SRP), and the innovative Mamba Aggregation (MA) mechanism, it achieves robust performance with reduced computational complexity. MambaPro sets new standards in handling long sequences and missing modalities.


News


Table of Contents


Introduction

Multi-modal object ReID leverages complementary data from diverse modalities (e.g., RGB, NIR, TIR) to overcome challenges like poor lighting and occlusion. MambaPro advances this field by:

  1. PFA: Transferring CLIP's pre-trained knowledge to ReID tasks via parallel adapters.
  2. SRP: Integrating modality-specific prompts with synergistic transformations.
  3. MA: Efficiently modeling intra- and inter-modality interactions with linear complexity.

Contributions


Results

Multi-Modal Object ReID

Multi-Modal Person ReID [RGBNT201]

<p align="center"> <img src="results/RGBNT201.png" alt="RGBNT201 Results" style="width:100%;"> </p>

Multi-Modal Vehicle ReID [RGBNT100 & MSVR310]

<p align="center"> <img src="results/RGBNT100_MSVR310.png" alt="Vehicle Results" style="width:100%;"> </p>

Ablation Studies [RGBNT201]

<p align="center"> <img src="results/Ablation.png" alt="Ablation Results" style="width:100%;"> </p>

Hyperparameter Analysis [RGBNT201]

<p align="center"> <img src="results/hyper.png" alt="Hyperparameter Analysis" style="width:100%;"> </p>

Visualizations

Feature Distribution (t-SNE)

<p align="center"> <img src="results/tsne.png" alt="t-SNE Distribution" style="width:100%;"> </p>

GradCam Visualization

<p align="center"> <img src="results/gradcam_person.png" alt="Prompt Comparisons" style="width:100%;"> </p> <p align="center"> <img src="results/gradcam_vehicle.png" alt="Prompt Comparisons" style="width:100%;"> </p>

Reproduction

Datasets

Pretrained Models

Configuration

Training

#!/bin/bash
# python = 3.10.13
# cuda = 11.8
source activate (your_env)
cd (your_path)
pip install -r requirements.txt
cd selective_scan && pip install .
python train_net.py --config_file configs/RGBNT201/MambaPro.yml

Note

Star History

Star History Chart


Citation

If you find MambaPro helpful in your research, please consider citing:

@inproceedings{wang2025MambaPro,
  title={MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt},
  author={Wang, Yuhao and Liu, Xuehu and Yan, Tianyu and Liu, Yang and Zheng, Aihua and Zhang, Pingping and Lu, Huchuan},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2025}
}