FlexiReID支持任意跨模态行人检索，提升实际应用灵活性。
FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification
- 采用自适应专家混合机制动态融合多模态特征
- 在7种检索模式下均达最优，显著优于现有方法
- 适合需要多模态检索的智能监控场景
多模态行人重识别旨在跨不同模态匹配行人图像。然而，现有方法大多仅支持有限的跨模态设置，难以实现任意查询-检索组合，限制了实际部署。本文提出FlexiReID，一个支持四种模态（rgb、红外、素描、文本）下七种检索模式的灵活框架。该框架引入自适应专家混合（MoE）机制，动态整合多样本模态特征，并设计跨模态查询融合模块以增强多模态特征提取。为促进全面评估，构建了CIRS-PEDES数据集，将四个主流Re-ID数据集扩展至包含全部四种模态。大量实验表明，FlexiReID在复杂场景中表现出色，达到当前最优性能并具备强泛化能力。
原文摘要 · Abstract (English)
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.
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