An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing

Unsupervised fine-grained image hashing aims to learn compact binary hash codes in unsupervised settings, addressing challenges posed by large-scale datasets and dependence on supervision. In this paper, we first identify a granularity gap between generic and fine-grained datasets for unsupervised hashing methods, highlighting the inadequacy of conventional self-supervised learning for fine-grained visual objects. To bridge this gap, we propose the Asymmetric Augmented Self-Supervised Learning (A$^2$-SSL) method, comprising three modules. The asymmetric augmented SSL module employs suitable augmentation strategies for positive/negative views, preventing fine-grained category confusion inherent in conventional SSL. Part-oriented dense contrastive learning utilizes the Fisher Vector framework to capture and model fine-grained object parts, enhancing unsupervised representations through part-level dense contrastive learning. Self-consistent hash code learning introduces a reconstruction task aligned with the self-consistency principle, guiding the model to emphasize comprehensive features, particularly fine-grained patterns. Experimental results on five benchmark datasets demonstrate the superiority of A$^2$-SSL over existing methods, affirming its efficacy in unsupervised fine-grained image hashing.