Enhancing fine-grained image classification through attentive batch training
Duy Le, Bao Q. Bui, Anh Tran, Cong Tran, Cuong Pham · ICT Express · 2025
Fine-grained image classification, which is a challenging task in computer vision, requires precise differentiation among visually similar object categories. In this paper, we design a novel framework, namely Relationship Batch Integration (RBI), allowing the discernment of vital visual features that may remain elusive when examining a singular image representative of a particular class. Our proposed method, validated through extensive experiments, significantly boosts the accuracy of fine-grained classifiers, achieving state-of-the-art performance with ( 97 . 79 % ) on the Stanford Dog dataset, even attaining a top result of ( 93 . 71 % ) on the Tiny-Imagenet dataset for general image classification.