Cheng Pang

TL;DR: This article proposes a dense lightweight network, called MADNet, for stronger multiscale feature expression and feature correlation learning, and presents a dual residual-path block (DRPB) that utilizes the hierarchical features from original low-resolution images.

TL;DR: A cascading residual network (CRN) that contains several locally sharing groups (LSGs) that not only promotes the propagation of features and the gradient but also eases the model training is proposed, which outperforms most of the advanced methods while still retaining a reasonable number of parameters.

TL;DR: A novel deep residual convolutional neural network (DRCNN) for image denoising with skip connections that reduces the path length of gradient transfer, making the gradient transfer in a short path and alleviating the vanishing-gradient problem.

TL;DR: A novel Bilinear Pyramid Network (BPN) for flower categorization is presented, where features from a convolutional layer are resized and multiplied with that from the former layer, which alternates multiple times to generates prediction vectors using the features from distinct layers.

TL;DR: A novel multi-scale rain removal model that adapts a two-branch squeeze-and-excitation residual network architecture that learns the basic structure and texture details of the corresponding clean image to effectively remove rain streaks from an image to restore its structural information and details.