activity
20102020
most citedUnitBox: An Advanced Object Detection Network

1.6k citations · 4.8k across the 90 of their papers we have counts for

collaborators

91 papers

cs.CV2020★ 15 cited

CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation

Yang Fu, Linjie Yang, Ding Liu +2

Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features…

cs.CV2020★ 9 cited

Neural Sparse Representation for Image Restoration

Yuchen Fan, Jiahui Yu, Yiqun Mei +4

Inspired by the robustness and efficiency of sparse representation in sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks. Our met…

eess.IV2020★ 5 cited

Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation

Hanchao Yu, Xiao Chen, Humphrey Shi +3

Cardiac motion estimation plays a key role in MRI cardiac feature tracking and function assessment such as myocardium strain. In this paper, we propose Motion Pyramid Networks, a n…

cs.CV2020

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte +87

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new versi…

eess.IV2020★ 9 cited

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

Kai Zhang, Shuhang Gu, Radu Timofte +60

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input im…

cs.CV2020★ 6 cited

The 1st Agriculture-Vision Challenge: Methods and Results

Mang Tik Chiu, Xingqian Xu, Kai Wang +39

The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially f…