Fine-Grained Few-Shot Image Classification Based on Feature Dual Reconstruction
Shudong Liu,
Wenlong Zhong,
Furong Guo
et al.
Abstract:Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories within a broader category using a limited number of samples. The challenge stems from the high intra-class variability and low inter-class variability of fine-grained images, which often hamper classification performance. To overcome this, we propose a fine-grained few-shot image classification algorithm based on bidirectional feature reconstruction. This algorithm introduces a Mix… Show more
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