Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916824289902592 |
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| author | Muthivhi, Mufhumudzi van Zyl, Terence L. |
| author_facet | Muthivhi, Mufhumudzi van Zyl, Terence L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings Muthivhi, Mufhumudzi van Zyl, Terence L. Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.02403 |