In both terrestrial and marine ecology, physical tagging is a frequently used
method to study population dynamics and behavior. However, such tagging
techniques are increasingly being replaced by individual re-identification
using image analysis.
This paper introduces a contrastive learning-based model for identifying
individuals. The model uses the first parts of the Inception v3 network,
supported by a projection head, and we use contrastive learning to find similar
or dissimilar image pairs from a collection of uniform photographs. We apply
this technique for corkwing wrasse, Symphodus melops, an ecologically and
commercially important fish species. Photos are taken during repeated catches
of the same individuals from a wild population, where the intervals between
individual sightings might range from a few days to several years.
Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56,
and a 100-shot accuracy of 0.88, on our dataset.

本研究提出了一种基于对比学习的模型，将物理标记技术替换为图像分析进行个体鉴定，其在对 Symphodus melops 的照片进行处理后，取得了较高的准确率。