Second-and higher-order statistics of data points have played an important role in advancing the state of the art on several computer vision problems such as the fine-grained image and scene recognition. However, these statistics need to be passed via an appropriate pooling scheme to obtain the best performance. Power Normalizations are non-linear activation units which enjoy probability-inspired derivations and can be applied in CNNs. In this paper, we propose a similarity learning network leveraging second-order information and Power Normalizations. To this end, we propose several formulations capturing second-order statistics and derive a sigmoid-like Power Normalizing function to demonstrate its interpretability. Our model is trained endto-end to learn the similarity between the support set and query images for the problem of one-and few-shot learning. The evaluations on Omniglot, miniImagenet and Open MIC datasets demonstrate that this network obtains stateof-the-art results on several few-shot learning protocols. We are the first to embed second-order statistics and Power Normalization into one-and few-shot learning. The relationship descriptors passed via such a non-linearity are then passed to a so-called similarity learning network.
Zhang et al. REF utilized second-order statistics with power normalization and permutation data augmentation to learn the similarity for few-shot learning.
Power Normalizing Second-Order Similarity Network for Few-Shot Learning
2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
Computer Science