FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912654676721664 |
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| author | Sun, Zhen Tan, Lei Shen, Yunhang Cai, Chengmao Sun, Xing Dai, Pingyang Cao, Liujuan Ji, Rongrong |
| author_facet | Sun, Zhen Tan, Lei Shen, Yunhang Cai, Chengmao Sun, Xing Dai, Pingyang Cao, Liujuan Ji, Rongrong |
| contents | Multimodal person re-identification (Re-ID) aims to match pedestrian images across different modalities. However, most existing methods focus on limited cross-modal settings and fail to support arbitrary query-retrieval combinations, hindering practical deployment. We propose FlexiReID, a flexible framework that supports seven retrieval modes across four modalities: rgb, infrared, sketches, and text. FlexiReID introduces an adaptive mixture-of-experts (MoE) mechanism to dynamically integrate diverse modality features and a cross-modal query fusion module to enhance multimodal feature extraction. To facilitate comprehensive evaluation, we construct CIRS-PEDES, a unified dataset extending four popular Re-ID datasets to include all four modalities. Extensive experiments demonstrate that FlexiReID achieves state-of-the-art performance and offers strong generalization in complex scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15595 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification Sun, Zhen Tan, Lei Shen, Yunhang Cai, Chengmao Sun, Xing Dai, Pingyang Cao, Liujuan Ji, Rongrong Computer Vision and Pattern Recognition Multimodal person re-identification (Re-ID) aims to match pedestrian images across different modalities. However, most existing methods focus on limited cross-modal settings and fail to support arbitrary query-retrieval combinations, hindering practical deployment. We propose FlexiReID, a flexible framework that supports seven retrieval modes across four modalities: rgb, infrared, sketches, and text. FlexiReID introduces an adaptive mixture-of-experts (MoE) mechanism to dynamically integrate diverse modality features and a cross-modal query fusion module to enhance multimodal feature extraction. To facilitate comprehensive evaluation, we construct CIRS-PEDES, a unified dataset extending four popular Re-ID datasets to include all four modalities. Extensive experiments demonstrate that FlexiReID achieves state-of-the-art performance and offers strong generalization in complex scenarios. |
| title | FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.15595 |