Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910730172760064 |
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| author | He, Lingfeng Cheng, De Wang, Nannan Gao, Xinbo |
| author_facet | He, Lingfeng Cheng, De Wang, Nannan Gao, Xinbo |
| contents | Unsupervised visible-infrared person re-identification (USL-VI-ReID) endeavors 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 between the feature space and the 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 quantify the inconsistency between the pseudo-label space and the feature space, subsequently minimizing it. The proposed MULT ensures that the generated pseudo-labels maintain alignment across modalities while upholding structural consistency within intra-modality. Additionally, a straightforward plug-and-play Online Cross-memory Label Refinement (OCLR) module is proposed to further mitigate the side effects of noisy pseudo-labels while simultaneously aligning different modalities, coupled with an Alternative Modality-Invariant Representation Learning (AMIRL) framework. Experiments demonstrate that our proposed method outperforms existing state-of-the-art USL-VI-ReID methods, highlighting the superiority of our MULT in comparison to other cross-modality association methods. Code is available at https://github.com/FranklinLingfeng/code_for_MULT. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_00672 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID He, Lingfeng Cheng, De Wang, Nannan Gao, Xinbo Computer Vision and Pattern Recognition Artificial Intelligence Unsupervised visible-infrared person re-identification (USL-VI-ReID) endeavors 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 between the feature space and the 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 quantify the inconsistency between the pseudo-label space and the feature space, subsequently minimizing it. The proposed MULT ensures that the generated pseudo-labels maintain alignment across modalities while upholding structural consistency within intra-modality. Additionally, a straightforward plug-and-play Online Cross-memory Label Refinement (OCLR) module is proposed to further mitigate the side effects of noisy pseudo-labels while simultaneously aligning different modalities, coupled with an Alternative Modality-Invariant Representation Learning (AMIRL) framework. Experiments demonstrate that our proposed method outperforms existing state-of-the-art USL-VI-ReID methods, highlighting the superiority of our MULT in comparison to other cross-modality association methods. Code is available at https://github.com/FranklinLingfeng/code_for_MULT. |
| title | Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2402.00672 |