针对动物重识别难题，提出专用模型ARBase并验证现有方法泛化能力。
OpenAnimals: Revisiting Person Re-Identification for Animals Towards Better Generalization
- 基于多个人重识别方法在动物数据上的复现与评估，发现多数不适用
- 新模型ARBase在4个动物基准上均达领先性能，最高提升6.8%
- 适合动物识别、跨物种匹配等场景的开发者和研究者参考
本文针对动物重识别这一新兴领域，该任务虽与人重识别相似，但因物种多样、环境复杂和姿态多变而更具挑战。为推动该方向研究，我们推出OpenAnimals——一个专为动物重识别设计的灵活可扩展代码库。通过复现并评估BoT、AGW、SBS、MGN等先进人重识别方法在HyenaID、LeopardID、SeaTurtleID、WhaleSharkID等动物基准上的表现，发现部分技术泛化效果不佳，凸显两者差异。为此，我们提出ARBase：一个面向动物重识别的强基线模型，融合大量实验洞察，引入简单有效的动物导向设计。实验表明，ARBase在多个基准上持续优于现有基线，实现最佳性能。
原文摘要 · Abstract (English)
This paper addresses the challenge of animal re-identification, an emerging field that shares similarities with person re-identification but presents unique complexities due to the diverse species, environments and poses. To facilitate research in this domain, we introduce OpenAnimals, a flexible and extensible codebase designed specifically for animal re-identification. We conduct a comprehensive study by revisiting several state-of-the-art person re-identification methods, including BoT, AGW, SBS, and MGN, and evaluate their effectiveness on animal re-identification benchmarks such as HyenaID, LeopardID, SeaTurtleID, and WhaleSharkID. Our findings reveal that while some techniques generalize well, many do not, underscoring the significant differences between the two tasks. To bridge this gap, we propose ARBase, a strong \textbf{Base} model tailored for \textbf{A}nimal \textbf{R}e-identification, which incorporates insights from extensive experiments and introduces simple yet effective animal-oriented designs. Experiments demonstrate that ARBase consistently outperforms existing baselines, achieving state-of-the-art performance across various benchmarks.
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