FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification
This addresses the limitation of existing methods that fail to support arbitrary query-retrieval combinations, which is crucial for practical deployment in surveillance and security applications.
The paper tackles the problem of multimodal person re-identification by proposing FlexiReID, a flexible framework that supports seven retrieval modes across four modalities, achieving state-of-the-art performance with strong generalization in complex scenarios.
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.