| Summary: | In recent years, few-shot fine-grained image classification has shown great potential in addressing data scarcity and distinguishing highly similar categories. However, existing unidirectional reconstruction methods, while enhancing inter-class differences, fail to effectively suppress intra-class variations; bidirectional reconstruction methods, although alleviating intra-class variations, inevitably introduce background noise. To overcome these limitations, this paper proposes a Bidirectional Feature Reconstruction Network that incorporates a Feature Enhancement Attention Module (FEAM) to highlight discriminative regions and suppress background interference, while integrating a Channel-Aware Spatial Attention (CASA) module to strengthen local feature modeling and compensate for the Transformer’s tendency to overemphasize global information. This joint design not only enhances inter-class separability but also effectively reduces intra-class variation. Extensive experiments on the CUB-200-2011, Stanford Cars, and Stanford Dogs datasets demonstrate that the proposed method consistently outperforms state-of-the-art approaches, validating its effectiveness and robustness in few-shot fine-grained image classification.
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