HGNet: Hash Generation Network Guided by High Frequency Information for Fine-Grained Image Retrieval

Abstract

Fine-grained image retrieval (FGIR) is an important topic of image retrieval, and its challenge lies in the accurate identification of image objects with minor inter-class differences and considerable intraclass differences. Most existing methods exploit Convolutional Neural Networks (CNNs) to capture fine-grained and coarse-grained information while overlooking the scale variations. To address these issues, a novel method named Hash Generation Network (HGNet) guided by high frequency information is developed to learn crucial details across different scales. The HGNet consists of a High-Frequency Guidance Module (HFGM) and a Hash Generation Module (HGM). The key contribution is the proposed HFGM which integrates the high-frequency information and multi-scale features extracted from the Swin Transformer. As the Swin Transformer can effectively capture global contextual information, its multi-scale features, guided by high-frequency information that contains fine-grained texture details, can represent both fine-grained and coarse-grained details, thereby guiding the HGM in generating discriminative hash codes. Experimental results show that the HGNet outperforms several SOTA FGIR methods in retrieval performance.