导读
the foreground-only measures foreground-supported retrieval;
英文标题：CounterFactual Retrieval for Evidence-Aware Individual Animal Identification
arXiv编号：2611.06864
作者：Ankit K. Upadhyay, Aleksandr Algasov, Ekaterina Nepovinnykh
来源：arXiv
原文链接：https://arxiv.org/abs/2611.06864
中文摘要：
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.
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