Feature-Aware Noise Contrastive Learning For Unsupervised Red Panda Re-Identification

arXiv:2405.00468v1 Announce Type: new
Abstract: To facilitate the re-identification (Re-ID) of individual animals, existing methods primarily focus on maximizing feature similarity within the same individual and enhancing distinctiveness between different individuals. However, most of them still rely on supervised learning and require substantial labeled data, which is challenging to obtain. To avoid this issue, we propose a Feature-Aware Noise Contrastive Learning (FANCL) method to explore an unsupervised learning solution, which is then validated on the task of red panda re-ID. …

abstract animals arxiv cs.ai cs.cv data feature focus however identification noise supervised learning them type unsupervised