Extended Cross-Modality United Learning for Unsupervised Visible-Infrared Person Re-identification

• URL: http://arxiv.org/abs/2412.19134v1
• Date: Thu, 26 Dec 2024 09:30:26 GMT
• Title: Extended Cross-Modality United Learning for Unsupervised Visible-Infrared Person Re-identification
• Authors: Ruixing Wu, Yiming Yang, Jiakai He, Haifeng Hu,
• Abstract summary: Unsupervised learning aims to learn modality-invariant features from unlabeled cross-modality datasets. Existing methods lack cross-modality clustering or excessively pursue cluster-level association. We propose Extended Cross-Modality United Learning (ECUL) framework, incorporating Extended Modality-Camera Clustering (EMCC) and Two-Step Memory Updating Strategy (TSMem) modules.
• Score: 34.93081601924748
• License:
• Abstract: Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims to learn modality-invariant features from unlabeled cross-modality datasets and reduce the inter-modality gap. However, the existing methods lack cross-modality clustering or excessively pursue cluster-level association, which makes it difficult to perform reliable modality-invariant features learning. To deal with this issue, we propose a Extended Cross-Modality United Learning (ECUL) framework, incorporating Extended Modality-Camera Clustering (EMCC) and Two-Step Memory Updating Strategy (TSMem) modules. Specifically, we design ECUL to naturally integrates intra-modality clustering, inter-modality clustering and inter-modality instance selection, establishing compact and accurate cross-modality associations while reducing the introduction of noisy labels. Moreover, EMCC captures and filters the neighborhood relationships by extending the encoding vector, which further promotes the learning of modality-invariant and camera-invariant knowledge in terms of clustering algorithm. Finally, TSMem provides accurate and generalized proxy points for contrastive learning by updating the memory in stages. Extensive experiments results on SYSU-MM01 and RegDB datasets demonstrate that the proposed ECUL shows promising performance and even outperforms certain supervised methods.

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