FM-ReID：面向目标重识别的选择性竞争令牌路由

中文摘要 AI 辅助

目标重识别（ReID）面临一个反复出现的挑战：不同身份可能具有高度相似的全局外观，而区分它们的线索是局部的、异质的，并且仅在特定视角下可见。这一挑战出现在动物重识别中的斑纹、轮廓和疤痕，人员重识别中微妙的衣物和配饰线索，以及车辆重识别中的局部外观细节。尽管视觉基础模型将此类信息编码在密集令牌中，但单一的全局描述符可能会掩盖判别性的局部信号。我们提出FM-ReID，一种端到端框架，将局部表示学习形式化为选择性竞争令牌路由。其竞争性细粒度挖掘模块使用多个挖掘查询和一个残差查询来竞争密集的DINOv3令牌。高于先验的选择保留优先分配给每个挖掘查询的令牌，而残差槽接收从检索描述符中排除的令牌。所得的多查询描述符与全局表示联合训练用于检索，无需固定的空间划分或等面积约束。FM-ReID在动物、人员和车辆重识别基准上取得了强劲的结果，支持竞争性令牌路由作为增强全局基础模型表示的有效方式。

英文摘要

Object re-identification (ReID) faces a recurring challenge: different identities can share highly similar global appearances, while the cues that distinguish them are localized, heterogeneous, and visible only under particular viewpoints. This challenge arises in animal ReID through markings, contours, and scars, in person ReID through subtle clothing and accessory cues, and in vehicle ReID through localized appearance details. Although visual foundation models encode such information in dense tokens, a single holistic descriptor can obscure discriminative local signals. We propose FM-ReID, an end-to-end framework that formulates local representation learning as selective competitive token routing. Its Competitive Fine-grained Mining module uses multiple mining queries and a residual query to compete for dense DINOv3 tokens. Above-prior selection retains tokens preferentially allocated to each mining query, while the residual slot receives tokens excluded from the retrieval descriptors. The resulting multi-query descriptors are jointly trained with a holistic representation for retrieval, without fixed spatial partitions or equal-area constraints. FM-ReID achieves strong results on animal, person, and vehicle ReID benchmarks, supporting competitive token routing as an effective way to augment holistic foundation-model representations.

发表机构

• Ocean University of China（中国海洋大学）
• Sanya Oceanographic Institution, Ocean University of China（中国海洋大学三亚海洋研究院）

机构由 AI 辅助整理，请以论文原文为准。

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12 pages