Xiaoqiong Li

TL;DR: SlimYOLOv3 as discussed by the authors proposes to learn efficient deep object detectors through channel pruning of convolutional layers, which enforce channel-level sparsity of CNNs by imposing L1 regularization on channel scaling factors and prune less informative feature channels to obtain "slim" object detectors.

TL;DR: A comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge is presented, revealing the shortcomings of current training strategies and highlighting the need for developing new optimal metrics to accurately quantify the clinical applicability of methods.

TL;DR: SlimYOLOv3 as mentioned in this paper proposes to learn efficient deep object detectors through channel pruning of convolutional layers, which enforce channel-level sparsity of CNNs by imposing L1 regularization on channel scaling factors and prune less informative feature channels to obtain "slim" object detectors.

TL;DR: A real-time detection and tracking system adopting Gaussian background model and compressive tracking algorithm was designed and implemented, which could meet future demands of the amphibious spherical robot in biological monitoring and multi-target tracking.

TL;DR: A novel conditional generative model, called CoSinGAN, which can be learned from a single radiological image with a given condition, i.e., the annotation mask of the lungs and infected regions is proposed, which has the potential to learn COVID-19 infection segmentation from few radiological images in the early stage of CO VID-19 pandemic.