Exploring Clustering Capability of Inpainting Model Embeddings for Pattern-based Individual Identification

ArXi:2605.04904v1 Announce Type: new In this paper, we explore deep learning techniques for individual identification of animals based on their skin patterns. Individual identification is crucial in biodiversity monitoring, since it enables analysis of decline or growth of populations, or intra-species interactions within populations. Models trained for the task of individual identification often do not focus on the skin pattern of animals, but on background details or body shape details. These characteristics are not individually specific, or can change drastically through time.