FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification
Abstract
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.
Cite
Text
Sun et al. "FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification." Proceedings of the 42nd International Conference on Machine Learning, 2025.Markdown
[Sun et al. "FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/sun2025icml-flexireid/)BibTeX
@inproceedings{sun2025icml-flexireid,
title = {{FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification}},
author = {Sun, Zhen and Tan, Lei and Shen, Yunhang and Cai, Chengmao and Sun, Xing and Dai, Pingyang and Cao, Liujuan and Ji, Rongrong},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pages = {57680-57693},
volume = {267},
url = {https://mlanthology.org/icml/2025/sun2025icml-flexireid/}
}