DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification

Yuhao Wang · Yang Liu · Aihua Zheng · Pingping Zhang*

AAAI 2025 Paper

DeMo is an advanced multi-modal object Re-Identification (ReID) framework designed to tackle dynamic imaging quality variations across modalities. By employing decoupled features and a novel Attention-Triggered Mixture of Experts (ATMoE), DeMo dynamically balances modality-specific and modality-shared information, enabling robust performance even under missing modality conditions. The framework sets new benchmarks for multi-modal and missing-modality object ReID.

News

• We released the DeMo codebase and paper! 🚀 Paper
• Great news! Our paper has been accepted to AAAI 2025! 🎉

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Table of Contents

• Introduction
• Contributions
• Results
• Visualizations
• Reproduction
• Citation

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Introduction

Multi-modal object ReID combines the strengths of different modalities (e.g., RGB, NIR, TIR) to achieve robust identification across challenging scenarios. DeMo introduces a decoupled approach using Mixture of Experts (MoE) to preserve modality uniqueness and enhance diversity. This is achieved through:

1. Patch-Integrated Feature Extractor (PIFE): Captures multi-granular representations.
2. Hierarchical Decoupling Module (HDM): Separates modality-specific and shared features.
3. Attention-Triggered Mixture of Experts (ATMoE): Dynamically adjusts feature importance with adaptive attention-guided weights.

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Contributions

• Introduced a decoupled feature-based MoE framework, DeMo, addressing dynamic quality changes in multi-modal imaging.
• Developed the Hierarchical Decoupling Module (HDM) for enhanced feature diversity and Attention-Triggered Mixture of Experts (ATMoE) for context-aware weighting.
• Achieved state-of-the-art performance on RGBNT201, RGBNT100, and MSVR310 benchmarks under both full and missing-modality settings.

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Results

Multi-Modal Object ReID

Multi-Modal Person ReID [RGBNT201]

Multi-Modal Vehicle ReID [RGBNT100 & MSVR310]

Missing-Modality Object ReID

Missing-Modality Performance [RGBNT201]

Missing-Modality Performance [RGBNT100]

Ablation Studies [RGBNT201]

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Visualizations

Feature Distribution (t-SNE)

Decoupled Features

Rank-list Visualization

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Reproduction

Datasets

• RGBNT201: Google Drive
• RGBNT100: Baidu Pan (Code: rjin)
• MSVR310: Google Drive

Pretrained Models

• ViT-B: Baidu Pan (Code: vmfm)
• CLIP: Baidu Pan (Code: 52fu)

Configuration

• RGBNT201: configs/RGBNT201/DeMo.yml
• RGBNT100: configs/RGBNT100/DeMo.yml
• MSVR310: configs/MSVR310/DeMo.yml

Training

conda create -n DeMo python=3.8.12 -y 
conda activate DeMo
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
cd (your_path)
pip install -r requirements.txt
python train_net.py --config_file configs/RGBNT201/DeMo.yml

Notes

• This repository is based on MambaPro. The prompt and adapter tuning on the CLIP backbone are reserved (the corresponding hyperparameters are set to False), allowing users to explore them independently.
• This code provides multi-modal Grad-CAM visualization, multi-modal ranking list generation, and t-SNE visualization tools to facilitate further research.
• The hyperparameter configuration is designed to ensure compatibility with devices equipped with less than 24GB of memory.
• Thank you for your attention and interest!

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Star History

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Citation

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

@inproceedings{wang2025DeMo,
  title={DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification},
  author={Wang, Yuhao and Liu, Yang and Zheng, Aihua and Zhang, Pingping},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2025}
}