面向证据感知的个体动物识别的反事实检索
CounterFactual Retrieval for Evidence-Aware Individual Animal Identification
Ankit K. Upadhyay, Aleksandr Algasov, Ekaterina Nepovinnykh, Fedor Zolotarev, Tuomas Eerola, Heikki Kälviäinen, Charles V. Stewart, Lukas Picek
AI 速读 基于摘要
提出反事实前景检索（CFR）评估协议，通过全图、仅前景、仅捷径三种视图把检索分数转化为证据画像，以检验模型是否依赖动物特征而非身份相关捷径。
cs.CV · cs.AI · cs.LG
查看原文摘要
Automated identification of individual animals is moving from small curated studies toward shared benchmarks and reusable foundation models. This progress is useful, but current evaluation leaves the evidence behind a match unresolved, i.e., high retrieval accuracy does not show whether a model used animal features or identity-correlated shortcuts. We address this with Counterfactual Foreground Retrieval (CFR), an evidence-aware evaluation protocol. CFR evaluates the same query-gallery task under different views: full image, foreground-only, and shortcut-only. The full-image measures standard retrieval performance; the foreground-only measures foreground-supported retrieval; and shortcut-only view measure how much identity signal is recoverable from background, or silhouette. This turns a retrieval score into an evidence profile: how well the model performs on the original images, how much of that performance remains when only the animal is visible, and how much identity signal remains recoverable when the animal is removed. We release around 0.5M masks for 34 datasets and code for mask generation and CFR evaluation.