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

Yuhao Wang; Yang Liu; Aihua Zheng; Pingping Zhang

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arXiv · 2412.10650

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

Yuhao Wang·Yang Liu·Aihua Zheng·Pingping Zhang

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Abstract

Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by combining complementary information from multiple modalities. Existing multi-modal object ReID methods primarily focus on the fusion of heterogeneous features. However, they often overlook the dynamic quality changes in multi-modal imaging. In addition, the shared information between different modalities can weaken modality-specific information. To address these issues, we propose a novel feature learning framework called DeMo for multi-modal object ReID, which adaptively balances decoupled features using a mixture of experts. To be specific, we first deploy a Patch-Integrated Feature Extractor (PIFE) to extract multi-granularity and multi-modal features. Then, we introduce a Hierarchical Decoupling Module (HDM) to decouple multi-modal features into non-overlapping forms, preserving the modality uniqueness and increasing the feature diversity. Finally, we propose an Attention-Triggered Mixture of Experts (ATMoE), which replaces traditional gating with dynamic attention weights derived from decoupled features. With these modules, our DeMo can generate more robust multi-modal features. Extensive experiments on three multi-modal object ReID benchmarks fully verify the effectiveness of our methods. The source code is available at https://github.com/924973292/DeMo.

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BibTeX RIS

Yuhao Wang, Yang Liu, Aihua Zheng, Pingping Zhang. 2024-12-14. DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification. https://arxiv.org/abs/2412.10650

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