Chenlin Zhang

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 has…