Feature-Aware Noise Contrastive Learning for Unsupervised Red Panda Re-Identification
This addresses the problem of identifying individual animals without labeled data for conservationists, but it is incremental as it adapts existing contrastive learning techniques to a specific domain.
The paper tackles unsupervised re-identification of red pandas by proposing Feature-Aware Noise Contrastive Learning (FANCL), which outperforms state-of-the-art unsupervised methods and achieves performance comparable to supervised learning.
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 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. FANCL designs a Feature-Aware Noise Addition module to produce noised images that conceal critical features, and employs two contrastive learning modules to calculate the losses. Firstly, a feature consistency module is designed to bridge the gap between the original and noised features. Secondly, the neural networks are trained through a cluster contrastive learning module. Through these more challenging learning tasks, FANCL can adaptively extract deeper representations of red pandas. The experimental results on a set of red panda images collected in both indoor and outdoor environments prove that FANCL outperforms several related state-of-the-art unsupervised methods, achieving high performance comparable to supervised learning methods.