Bi-channel attention meta learning for few-shot fine-grained image recognition

Yao Wang, Yang Ji, Wei Wang, Bailing Wang

Abstract: Highlights•An end-to-end model is proposed to address few-shot fine-grained recognition.•Develop a feature calibration module based on bi-channel attention blocks.•Propose an effective embedding module that learns better descriptors.