activity
20182022
most citedCOVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution

115 citations · 187 across the 10 of their papers we have counts for

collaborators

18 papers

cs.CV202211 cited

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw +85

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conj…

cs.LG2022

Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning

Dong Gong, Qingsen Yan, Yuhang Liu +2

Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL ap…

eess.IV2020115 cited

COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution

Qingsen Yan, Bo Wang, Dong Gong +7

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) i…

cs.CV20208 cited

Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory

Xinyu Zhang, Dong Gong, Jiewei Cao +1

Unsupervised cross-domain person re-identification (Re-ID) aims to adapt the information from the labelled source domain to an unlabelled target domain. Due to the lack of supervis…

cs.CV20201 cited

Semi-supervised Learning via Conditional Rotation Angle Estimation

Hai-Ming Xu, Lingqiao Liu, Dong Gong

Self-supervised learning (SlfSL), aiming at learning feature representations through ingeniously designed pretext tasks without human annotation, has achieved compelling progress i…

eess.IV20204 cited

Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation

Dong Gong, Wei Sun, Qinfeng Shi +2

Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs. The SR methods…