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Dynamic Enhancement Network for Partial Multi-modality Person Re-identification

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arxiv 2305.15762 v1 pith:6HIRQ5YQ submitted 2023-05-25 cs.CV

Aihua Zheng , Ziling He , Zi Wang , Chenglong Li , Jin Tang This is my paper · ORCID
classification cs.CV
keywords multi-modalitymissingmodalitiespersonre-iddynamicenhancementrepresentation
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Many existing multi-modality studies are based on the assumption of modality integrity. However, the problem of missing arbitrary modalities is very common in real life, and this problem is less studied, but actually important in the task of multi-modality person re-identification (Re-ID). To this end, we design a novel dynamic enhancement network (DENet), which allows missing arbitrary modalities while maintaining the representation ability of multiple modalities, for partial multi-modality person Re-ID. To be specific, the multi-modal representation of the RGB, near-infrared (NIR) and thermal-infrared (TIR) images is learned by three branches, in which the information of missing modalities is recovered by the feature transformation module. Since the missing state might be changeable, we design a dynamic enhancement module, which dynamically enhances modality features according to the missing state in an adaptive manner, to improve the multi-modality representation. Extensive experiments on multi-modality person Re-ID dataset RGBNT201 and vehicle Re-ID dataset RGBNT100 comparing to the state-of-the-art methods verify the effectiveness of our method in complex and changeable environments.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-Modal Object Re-Identification with Prompt-S6 and Semantic-Aware Knowledge Guidance

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Prompt-S6 plus semantic token pruning and progressive tri-modal fusion improves multi-spectral object ReID accuracy and efficiency on four benchmarks.

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

    cs.CV 2024-12 conditional novelty 6.0 of 10

    MambaPro combines CLIP with a parallel adapter, synergistic residual prompts, and Mamba aggregation to achieve state-of-the-art mAP on RGBNT201, RGBNT100, and MSVR310.

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

    cs.CV 2024-12 conditional novelty 6.0 of 10

    DeMo improves multi-modal object re-identification by decoupling RGB, NIR, and TIR features into seven attention-derived streams and weighting them with an attention-triggered mixture of experts.

  4. Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A unified taxonomy and survey of single- to multi-modal person ReID, plus a Transformer-based VI-ReID baseline that is solid but not state-of-the-art.

  5. Multi-Modal Object Re-Identification with Dual Semantic Guidance and Global-Local Mutual Modulation

    cs.CV 2026-07 conditional novelty 4.0 of 10

    A dual-semantic (text + soft mask) global-local mutual modulation framework reports SOTA mAP/Rank-1 on RGBNT201, RGBNT100, and MSVR310.

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