Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings

• URL: http://arxiv.org/abs/2507.02403v1
• Date: Thu, 03 Jul 2025 07:56:54 GMT
• Title: Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings
• Authors: Mufhumudzi Muthivhi, Terence L. van Zyl,
• Abstract summary: Wildlife re-identification aims to match individuals of the same species across different observations.<n>Current state-of-the-art (SOTA) models rely on class labels to train supervised models for individual classification.<n>This study investigates self-supervised learning Self-Supervised Learning (SSL) for wildlife re-identification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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