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.
Abstract (translated)
多模态行人重识别（Re-ID）旨在匹配不同模态下的行人图像。然而，大多数现有方法主要关注有限的跨模态设置，并且无法支持任意查询-检索组合，这阻碍了其实用部署。我们提出了FlexiReID，这是一个灵活框架，支持四种模式（RGB、红外、素描和文本）之间的七种检索模式。FlexiReID引入了一种自适应专家混合（MoE）机制，以动态集成不同模态的特征，并设计了一个跨模态查询融合模块来增强多模态特征提取。为了促进全面评估，我们构建了CIRS-PEDES数据集，这是一个统一的数据集，扩展了四个流行的Re-ID数据集，包含了所有四种模式。广泛的实验表明，FlexiReID在复杂场景中达到了最先进的性能，并且具有强大的泛化能力。
URL
https://arxiv.org/abs/2510.15595
