Fine-Grained Augmentation and Progressive Feature Integration for Unsupervised Fine-Grained Hashing

Abstract

Unsupervised fine-grained image retrieval aims to retrieve specific subcategory images from large-scale unlabeled databases. The small inter-class and large intra-class variances inherent in fine-grained images present significant challenges for unsupervised model training and feature recognition. Without the guidance of supervised information, existing methods often fail to focus on fine-grained details, and multi-region features struggle to embed effectively into hash codes. In this article, we propose Fine-Grained Augmentation and Progressive Feature Integration for unsupervised fine-grained hashing, named FAPI. Specifically, from the perspective of unsupervised contrastive learning, we design fine-grained feature augmentation and cross-contrastive learning modules to enhance the capture of critical discriminative details. Additionally, from a feature extraction standpoint, we propose a progressive granularity feature integration module to extract and fuse multi-layer, multi-granularity features, ensuring effective fine-grained feature extraction and hash code embedding. Extensive experiments on five widely recognized fine-grained datasets demonstrate that FAPI significantly outperforms existing unsupervised methods, achieving state-of-the-art performance.