提出诊断框架，看清模型认的是豹子还是背景。
Are We Recognizing the Jaguar or Its Background? A Diagnostic Framework for Jaguar Re-Identification
- 用修复背景和前景图对比，测模型依赖背景还是毛纹
- 发现主流模型多靠背景而非毛纹识别，准确率仍高但不可靠
- 适合关注动物识别可信度的研究者与保护应用开发者
从公众科学图像中进行美洲豹重识别（re-ID）在标准检索指标上表现良好，但可能依赖错误线索，如背景环境或轮廓形状，而非定义身份的皮毛图案。本文提出一个双轴诊断框架：一是基于修复背景图与前景图的泄漏控制上下文比，衡量模型对背景/前景的依赖程度；二是基于跨侧检索与镜像自相似性的侧向性诊断。为使诊断可量化，构建了包含像素级分割掩码的潘塔纳尔美洲豹基准数据集，并设计身份均衡评估协议。以ArcFace微调、反对称正则化和洛伦兹双曲嵌入等代表性缓解方法为例，在同一评估视角下分析其行为。目标不仅是比较模型排名，更在于揭示其使用何种视觉证据进行判断。
原文摘要 · Abstract (English)
Jaguar re-identification (re-ID) from citizen-science imagery can look strong on standard retrieval metrics while still relying on the wrong evidence, such as background context or silhouette shape, instead of the coat pattern that defines identity. We introduce a diagnostic framework for wildlife re-ID with two axes: a leakage-controlled context ratio, background/foreground, computed from inpainted background-only versus foreground-only images, and a laterality diagnostic based on cross-flank retrieval and mirror self-similarity. To make these diagnostics measurable, we curate a Pantanal jaguar benchmark with per-pixel segmentation masks and an identity-balanced evaluation protocol. We then use representative mitigation families, ArcFace fine-tuning, anti-symmetry regularization, and Lorentz hyperbolic embeddings, as case studies under the same evaluation lens. The goal is not only to ask which model ranks best, but also what visual evidence it uses to do so.
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