Multi-scale Comparison Network for Few-Shot Learning

Few-shot learning, which learns from a small number of samples, is an emerging field in multimedia. Through systematically exploring influences of scale information, including multi-scale feature extraction, multi-scale comparison and increased parameters brought by multiple scales, in this paper, we present a novel end-to-end model called Multi-scale Comparison Network (MSCN) for few-shot learning. The proposed MSCN uses different scale convolutions for comparison to solve the problem of excessive gaps between target sizes in the images during few-shot learning. It first uses a 4-layer encoder to encode support and testing samples to obtain their feature maps. After deep splicing these feature maps, the proposed MSCN further uses a comparator comprising two layers of multi-scale comparative modules and two fully connected layers to derive the similarity between support and testing samples. Experimental results on two benchmark datasets including Omniglot and \(\textit{mini}\)Imagenet shows the effectiveness of the proposed MSCN, which has averagely 2% improvement on \(\textit{mini}\)Imagenet in all experimental results compared with the recent Relation Network.