Zhenbing Liu

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: This paper puts forward an automated nuclei segmentation method that works with hematoxylin and eosin stained breast cancer histopathology images, which represent regions of whole digital slides and achieves a promising segmentation performance, equivalent and sometimes surpassing recently published leading alternative segmentation methods with the same benchmark datasets.

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: This paper proposes a novel classifier, called the prior knowledge-based probabilistic collaborative representation-based classifier (PKPCRC), for visual recognition that outperforms several state-of-the-art classifiers.

TL;DR: The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.