A contrastive learning approach for individual re-identification in a wild fish population
This addresses the problem of replacing physical tagging with image analysis for ecologists studying fish populations, though it is incremental as it applies an existing method to a new domain.
The paper tackles individual re-identification of wild fish using a contrastive learning model, achieving a one-shot accuracy of 0.35, 5-shot accuracy of 0.56, and 100-shot accuracy of 0.88 on a dataset of corkwing wrasse.
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.