Enhancing Fine-grained Image Classification through Attentive Batch Training

• URL: http://arxiv.org/abs/2412.19606v1
• Date: Fri, 27 Dec 2024 12:07:58 GMT
• Title: Enhancing Fine-grained Image Classification through Attentive Batch Training
• Authors: Duy M. Le, Bao Q. Bui, Anh Tran, Cong Tran, Cuong Pham,
• Abstract summary: We propose a novel module called Residual Relationship Attention (RRA) to integrate visual feature vectors of batch images. We also design a novel framework called Relationship Position, which encodes the positions of relationships between original images in a batch. Our proposed method demonstrates significant improvements in the accuracy of different fine-grained classifiers, with an average increase of $(+2.78%)$ and $(+3.83%)$ on the CUB200-2011 and Stanford Dog datasets.
• Score: 3.9677818965537983
• License:
• Abstract: Fine-grained image classification, which is a challenging task in computer vision, requires precise differentiation among visually similar object categories. In this paper, we propose 1) a novel module called Residual Relationship Attention (RRA) that leverages the relationships between images within each training batch to effectively integrate visual feature vectors of batch images and 2) a novel technique called Relationship Position Encoding (RPE), which encodes the positions of relationships between original images in a batch and effectively preserves the relationship information between images within the batch. Additionally, we design a novel framework, namely Relationship Batch Integration (RBI), which utilizes RRA in conjunction with RPE, allowing the discernment of vital visual features that may remain elusive when examining a singular image representative of a particular class. Through extensive experiments, our proposed method demonstrates significant improvements in the accuracy of different fine-grained classifiers, with an average increase of $(+2.78\%)$ and $(+3.83\%)$ on the CUB200-2011 and Stanford Dog datasets, respectively, while achieving a state-of-the-art results $(95.79\%)$ on the Stanford Dog dataset. Despite not achieving the same level of improvement as in fine-grained image classification, our method still demonstrates its prowess in leveraging general image classification by attaining a state-of-the-art result of $(93.71\%)$ on the Tiny-Imagenet dataset. Furthermore, our method serves as a plug-in refinement module and can be easily integrated into different networks.

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