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Enhancing fine-grained image classification through attentive batch training
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DOI：10.1016/j.icte.2025.10.002.png)
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Abstract
En 中文
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.
Keywords:
Computer vision
Fine-grained image classification
Deep learning
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