用自监督学习实现野外动物个体识别，无需人工标注
Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings
- 从相机陷阱视频中自动提取时间序列图像对构建双视图
- 在有限数据下仍比监督模型更鲁棒，且在所有任务中表现更好
- 适合缺乏标注数据的野生动物研究者使用
野生动物重识别旨在匹配同一物种在不同观测中的个体。现有最先进模型依赖类别标签训练监督模型进行个体分类，这推动了大规模野生动物数据集的构建。本研究探索自监督学习（SSL）在野生动物重识别中的应用。我们无需监督地从相机陷阱数据中自动提取个体的时间图像对，作为双视图训练自监督模型，可利用潜在无限的视频流数据。我们在开放世界场景和多种野生动物下游任务中评估了学习到的表征性能，结果表明自监督模型在数据有限时更具鲁棒性，且在所有下游任务中均优于监督特征。代码已公开：https://github.com/pxpana/SSLWildlife。
原文摘要 · Abstract (English)
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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