[2506.06719] Improving Wildlife Out-of-Distribution Detection: Africas Big Five

Computer Science > Computer Vision and Pattern Recognition arXiv:2506.06719 (cs) [Submitted on 7 Jun 2025 (v1), last revised 1 Mar 2026 (this version, v2)] Title:Improving Wildlife Out-of-Distribution Detection: Africas Big Five Authors:Mufhumudzi Muthivhi, Jiahao Huo, Fredrik Gustafsson, Terence L. van Zyl View a PDF of the paper titled Improving Wildlife Out-of-Distribution Detection: Africas Big Five, by Mufhumudzi Muthivhi and 3 other authors View PDF HTML (experimental) Abstract:Mitigating human-wildlife conflict seeks to resolve unwanted encounters between these parties. Computer Vision provides a solution to identifying individuals that might escalate into conflict, such as members of the Big Five African animals. However, environments often contain several varied species. The current state-of-the-art animal classification models are trained under a closed-world assumption. They almost always remain overconfident in their predictions even when presented with unknown classes. This study investigates out-of-distribution (OOD) detection of wildlife, specifically the Big Five. To this end, we select a parametric Nearest Class Mean (NCM) and a non-parametric contrastive learning approach as baselines to take advantage of pretrained and projected features from popular classification encoders. Moreover, we compare our baselines to various common OOD methods in the literature. The results show feature-based methods reflect stronger generalisation capability across varying c...