Wan, Xixi, Zheng, Aihua ORCID: https://orcid.org/0000-0002-9820-4743, Wang, Zi ORCID: https://orcid.org/0000-0002-8001-0318, Jiang, Bo ORCID: https://orcid.org/0000-0002-6238-1596, Tang, Jin ORCID: https://orcid.org/0000-0001-8375-3590 and Ma, Jixin ORCID: https://orcid.org/0000-0001-7458-7412 (2025) Reliable multi-modal object re-identification via modality-aware graph reasoning. IEEE Transactions on Information Forensics and Security. ISSN 1556-6013 (Print), 1556-6021 (Online) (doi:10.1109/TIFS.2026.3696569)

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Abstract

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in the Re-Identification (ReID) task. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in cases where features are of low quality. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multimodal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing
disparities between different modalities. Experimental results on
four benchmarks (RGBNT201, Market1501-MM, RGBNT100,
MSVR310) indicate that the proposed method achieves stateof-
the-art performance in multi-modal object ReID. Our code is
available at https://github.com/wanxixi11/MGRNet.

Item Type:	Article
Additional Information:	Preprint: https://arxiv.org/abs/2504.14847
Uncontrolled Keywords:	Multi-modal Object Re-Identification, Modalityaware Graph, Selective Graph Nodes Swap, Graph Reasoning Network, Modality Missing
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	27 May 2026 16:26
URI:	https://gala.gre.ac.uk/id/eprint/53607

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