SaberNet: Self-attention based effective relation network for few-shot learning

Highlights：

• We design a novel Self-attention Based Effective Relation Network for few-shot learning and leverage relations not only from local details in feature extraction, but also from support samples and from prototype-query pair channels.

• We argue the insufficiency of conventional feature extraction in few-shot learning and demonstrate the effectiveness of self-attention in feature extraction. The proposed SaberNet can infer feature relations and model spatial long-range dependencies across features.

• Extensive experiments and analyses demonstrate the effectiveness of the proposed framework. And Saber network achieves superior performance over other state-of-the-art methods on three challenging datasets. Moreover, we present a simple and powerful baseline to investigate the effect of the backbone in few-shot learning.