标题： 自监督学习在相机陷阱视频上的应用产生了一个强大的通用面部嵌入器 显示英文标题

标题： Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder

摘要： 相机陷阱正在通过捕获大量视觉数据来革新野生动物监测；然而，个体动物的手动识别仍然是一个重要的瓶颈。 本研究介绍了一种完全自监督的方法，从无标签的相机陷阱视频中学习稳健的黑猩猩面部嵌入。 利用DINOv2框架，我们在自动挖掘的面部裁剪图像上训练视觉变换器，消除了对身份标签的需求。 我们的方法在开放集重新识别任务中表现出色，在Bossou等具有挑战性的基准测试中超越了监督基线，尽管在训练过程中没有使用任何标记数据。 这项工作强调了自监督学习在生物多样性监测中的潜力，并为可扩展的、非侵入性的种群研究铺平了道路。 显示英文摘要

摘要： Camera traps are revolutionising wildlife monitoring by capturing vast amounts of visual data; however, the manual identification of individual animals remains a significant bottleneck. This study introduces a fully self-supervised approach to learning robust chimpanzee face embeddings from unlabeled camera-trap footage. Leveraging the DINOv2 framework, we train Vision Transformers on automatically mined face crops, eliminating the need for identity labels. Our method demonstrates strong open-set re-identification performance, surpassing supervised baselines on challenging benchmarks such as Bossou, despite utilising no labelled data during training. This work underscores the potential of self-supervised learning in biodiversity monitoring and paves the way for scalable, non-invasive population studies.