Recently, deep convolutional neural network (CNN) have achieved promising performance for single image super-resolution (SISR). However, they usually extract features on a single scale and lack sufficient supervision information, leading to undesired artifacts and unpleasant noise in super-resolution (SR) images. To address this problem, we first propose a hierarchical feature extraction module (HFEM) to extract the features in multiple scales, which helps concentrate on both local textures and global semantics. Then, a hierarchical guided reconstruction module (HGRM) is introduced to reconstruct more natural structural textures in SR images via intermediate supervisions in a progressive manner. Finally, we integrate HFEM and HGRM in a simple yet efficient end-to-end framework named hierarchical generative adversarial networks (HSR-GAN) to recover consistent details, and thus obtain the semantically reasonable and visually realistic results. Extensive experiments on five common datasets demonstrate that our method shows favorable visual quality and superior quantitative performance compared to state-of-the-art methods for SISR.

Few-shot learning aims to learn a model that can be readily adapted to new unseen classes (concepts) by accessing one or few examples. Despite the successful progress, most of the few-shot learning approaches, concentrating on either global or local characteristics of examples, still suffer from weak generalization abilities. Inspired by the inverted pyramid theory, to address this problem, we propose an inverted pyramid network (IPN) that intimates the human's coarse-to-fine cognition paradigm. The proposed IPN consists of two consecutive stages, namely global stage and local stage. At the global stage, a class-sensitive contextual memory network (CCMNet) is introduced to learn discriminative support-query relation embeddings and predict the query-to-class similarity based on the contextual memory. Then at the local stage, a fine-grained calibration is further appended to complement the coarse relation embeddings, targeting more precise query-to-class similarity evaluation. To the best of our knowledge, IPN is the first work that simultaneously integrates both global and local characteristics in few-shot learning, approximately imitating the human cognition mechanism. Our extensive experiments on multiple benchmark datasets demonstrate the superiority of IPN, compared to a number of state-of-the-art approaches.