Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID

• URL: http://arxiv.org/abs/2402.00672v2
• Date: Sun, 4 Feb 2024 15:39:34 GMT
• Title: Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID
• Authors: Lingfeng He, De Cheng, Nannan Wang, Xinbo Gao
• Abstract summary: Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to retrieve pedestrian images of the same identity from different modalities without annotations. We introduce a Modality-Unified Label Transfer (MULT) module that simultaneously accounts for both homogeneous and heterogeneous fine-grained instance-level structures. It models both homogeneous and heterogeneous affinities, leveraging them to define the inconsistency for the pseudo-labels and then minimize it, leading to pseudo-labels that maintain alignment across modalities and consistency within intra-modality structures.
• Score: 62.81466902601807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to retrieve pedestrian images of the same identity from different modalities without annotations. While prior work focuses on establishing cross-modality pseudo-label associations to bridge the modality-gap, they ignore maintaining the instance-level homogeneous and heterogeneous consistency in pseudo-label space, resulting in coarse associations. In response, we introduce a Modality-Unified Label Transfer (MULT) module that simultaneously accounts for both homogeneous and heterogeneous fine-grained instance-level structures, yielding high-quality cross-modality label associations. It models both homogeneous and heterogeneous affinities, leveraging them to define the inconsistency for the pseudo-labels and then minimize it, leading to pseudo-labels that maintain alignment across modalities and consistency within intra-modality structures. Additionally, a straightforward plug-and-play Online Cross-memory Label Refinement (OCLR) module is proposed to further mitigate the impact of noisy pseudo-labels while simultaneously aligning different modalities, coupled with a Modality-Invariant Representation Learning (MIRL) framework. Experiments demonstrate that our proposed method outperforms existing USL-VI-ReID methods, highlighting the superiority of our MULT in comparison to other cross-modality association methods. The code will be available.

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