{"ID":2898648,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.02403","arxiv_id":"2507.02403","title":"Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings","abstract":"Wildlife re-identification aims to match individuals of the same species across different observations. Current state-of-the-art (SOTA) models rely on class labels to train supervised models for individual classification. This dependence on annotated data has driven the curation of numerous large-scale wildlife datasets. This study investigates self-supervised learning Self-Supervised Learning (SSL) for wildlife re-identification. We automatically extract two distinct views of an individual using temporal image pairs from camera trap data without supervision. The image pairs train a self-supervised model from a potentially endless stream of video data. We evaluate the learnt representations against supervised features on open-world scenarios and transfer learning in various wildlife downstream tasks. The analysis of the experimental results shows that self-supervised models are more robust even with limited data. Moreover, self-supervised features outperform supervision across all downstream tasks. The code is available here https://github.com/pxpana/SSLWildlife.","short_abstract":"Wildlife re-identification aims to match individuals of the same species across different observations. Current state-of-the-art (SOTA) models rely on class labels to train supervised models for individual classification. This dependence on annotated data has driven the curation of numerous large-scale wildlife dataset...","url_abs":"https://arxiv.org/abs/2507.02403","url_pdf":"https://arxiv.org/pdf/2507.02403v1","authors":"[\"Mufhumudzi Muthivhi\",\"Terence L. van Zyl\"]","published":"2025-07-03T07:56:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":612421,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2898648,"paper_url":"https://arxiv.org/abs/2507.02403","paper_title":"Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings","repo_url":"https://github.com/pxpana/SSLWildlife","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}