arXiv cs.AI· Ankit K. Upadhyay, Aleksandr Algasov, Ekaterina Nepovinnykh, Fedor Zolotarev, Tuomas Eerola, Heikki K\"alvi\"ainen, Charles V. Stewart, Lukas Picek·· 4 小时前AI 评分34
反事实检索 CFR：证据感知的个体动物识别评估协议
CounterFactual Retrieval for Evidence-Aware Individual Animal Identification
AI 导读
研究者提出 Counterfactual Foreground Retrieval（CFR），一种针对个体动物识别的证据感知评估协议。CFR 在全图、仅前景、仅捷径三种视角下评估同一检索任务，将检索分数转化为证据画像，揭示模型是依赖动物特征还是身份相关捷径。团队开源了覆盖 34 个数据集约 0.5M 个掩码及掩码生成与评估代码。
正文 · 原文
Abstract: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.
| Comments: | 14 pages, 5 figures; accepted at WACV 2027 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.06864 [cs.CV] |
| (or arXiv:2611.06864v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06864 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ankit Kumar Upadhyay [view email]
[v1]
Wed, 4 Nov 2026 18:19:26 UTC (4,376 KB)
来源：arXiv cs.AI · arxiv.org