Animal identification method works across different species and environments
Cross-Species Animal Re-Identification with Semantic Consistency Learning
Computer Vision and Pattern Recognition
Summary
Recognizing individual animals from photos can be very hard when those animals belong to different species with very different shapes and colors. The authors propose a new technique that learns stable features about animals, ignoring environmental differences like background or lighting, and finds relationships between animals across species. Their method improves accuracy in identifying animals even if the species or environment were not seen before. This work helps make animal identification more reliable in real-world scenarios.
What this means in practice
- •For wildlife monitoring teams: Identify individual animals across species in diverse habitats using stable, transferable visual features improving tracking and study of wildlife populations.
- •For conservation organizations: Use cross-species identification to monitor endangered animals more accurately even when data from new species or locations become available.
Authors
Shuoyi Chen, Yuejia Li, Mang Ye
Abstract
Generalizable animal Re-Identification (ReID) aims to recognize individual animals across species with diverse morphologies and ecological contexts. Unlike person ReID, where different domains share similar body structures, animal species often exhibit drastically different anatomical structures and visual patterns, making it difficult to establish shared visual correspondences. As a result, representations learned across species tend to form fragmented embedding spaces, which severely limits cross-species generalization. To address this challenge, we propose Semantic Consistency Learning (SCL), a framework designed to learn representations that remain stable across appearance variations while preserving semantic structures shared across species. SCL consists of two complementary components. Foreground-Background Decoupled Spectral Normalization (FDSNorm) stabilizes feature statistics by suppressing environment-induced style variations in a region-aware manner, while Cross-species Neighborhood Modeling (CNM) captures transferable relational structures across species through dynamic feature neighborhoods. Extensive experiments on 11 public animal ReID datasets demonstrate that SCL consistently outperforms state-of-the-art methods under multiple cross-species evaluation protocols and generalizes effectively to previously unseen species and ecological domains. Code is available at https://github.com/Kemalau/ECCV-26-SCL.