目标泛化重识别：迈向通用实例感知的一步
Object-Generalized Re-Identification: A Step Towards Universal Instance Perception
把元学习当作语义分布正则，让模型在受控的类别偏移下学习跨类别的身份不变表征；做 ReID 或任何需要跨类别身份匹配的视觉任务时，可直接复用这一训练范式，无需目标域自适应。
原文依据 treats meta-learning as semantic distributional regularization, exposing the model to controlled category shifts
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论目标重识别（ReID）任务旨在跨不同视角与传感条件识别同一个体目标。尽管行人重识别与车辆重识别已取得显著成功，但现有方法大多建立在训练数据与测试数据来自同一目标类别的假设之上。这一约束要求为每个类别单独训练模型，限制了可扩展性与泛化能力。为缓解这一局限，我们提出目标泛化重识别（OG-ReID），这是一种学习可跨不同目标类别迁移的统一身份表征的新范式。与关注单一类别内部外观变化的传统域泛化不同，OG-ReID 处理的是由身份线索的内在结构差异所导致的类别偏移。为实现这一目标，我们引入元泛化目标重识别（MGOR）框架，该框架将元学习视为语义分布正则化，使模型暴露于受控的类别偏移之下，从而让不变性在语义多样性与身份判别性之间的平衡中自然涌现。在来自多个领域的 100 余个未见目标类别上的大量评估表明，MGOR 在无需任何目标域自适应的情况下优于现有重识别方法，朝着超越领域与类别边界的通用身份感知迈进一步。
英文原文摘要 1400 字符
The object re-identification (ReID) task aims to recognize the same individual object across diverse viewpoints and sensing conditions.Although person and vehicle ReID have achieved remarkable success, most existing methods are built on the assumption that training and testing data come from the same object category.This constraint requires separate models for each category, which limits scalability and generalization.To address this limitation, we introduce Object-Generalized Re-Identification (OG-ReID), a new paradigm that learns unified identity representations transferable across different object categories.Unlike conventional domain generalization that focuses on appearance variations within a single category, OG-ReID deals with category shifts caused by intrinsic structural differences in identity cues. To achieve this goal, we introduce the Meta-Generalized Object Re-Identification (MGOR) framework, which treats meta-learning as semantic distributional regularization, exposing the model to controlled category shifts so that invariance emerges as an equilibrium between semantic diversity and identity discrimination.Extensive evaluations on more than 100 unseen object categories from multiple domains show that MGOR outperforms existing ReID approaches without any target-domain adaptation, advancing toward universal identity perception beyond domain and category boundaries.