Yunxin Zhong

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: 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: The Vision Meets Drone Object Detection in Image Challenge (VME-DET 2019) as discussed by the authors, held in conjunction with the 17th International Conference on Computer Vision (ICCV 2019), focuses on image object detection on drones.

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.

TL;DR: This paper surveys the key SSL techniques that help relieve the suffering of deep learning by combining with the development of related techniques in computer vision applications and includes the explanation methods for deep models that are important to clinical decision making.