AW-PCNN: Adaptive Weighting Pyramidal Convolutional Neural Network for Fine-Grained Few-Shot Learning

Abstract

Due to the unique challenges of high intra-class variation and low inter-class variation, Fine-Grained Visual Classification (FGVC) tasks extremely need to extract multilevel semantic features of fine-grained images for classification. Moreover, labor-intensive annotations and the existence of long-tailed distribution of fine-grained images in real life make fine-grained few-shot learning an urgent problem to be solved. In this paper, we propose an Adaptive Weighting Pyramidal Convolutional Neural Network (AW-PCNN) for fine-grained few-shot learning. Our AW-PCNN consists of a PCNN module and a AW module, which are improved in two aspects. First, our PCNN module extracts the features of each layer in CNN to obtain both high-level global and low-level local subtle features of images to overcome the challenge of FGVC tasks. Second, We employ the metric learning approach for few-shot learning, and our AW module improves it by selecting decisive pairs and adaptively weighting the pairs based on their similarity scores to mitigate the challenge of FGVC tasks and learn a better embedding space. Our AW-PCNN achieves state-of-the-art performance on three benchmark fine-grained datasets, which proves the effectiveness and superiority of our model.