Learning attention-guided pyramidal features for few-shot fine-grained recognition

Highlights：

• We propose a two-stage meta-learning framework to learn attention-guided pyramidal features for few-shot fine-grained recognition.

• We utilize a multi-scale feature pyramid and a multi-level attention pyramid to extract diverse features from different granularities.

• An attention-guided refinement strategy is proposed to enhance the dominative object and eliminate the negative interference of backgrounds.

• Extensive experiments demonstrate that the proposed framework significantly improves the performance of few-shot fine-grained recognition.