activity
20152026
most citedCollaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution

18 citations · 70 across the 30 of their papers we have counts for

collaborators
Showing 2020Show all

7 papers · 1 filter

cs.CV2020

Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection

Carlo Biffi, Steven McDonagh, Philip Torr +2

Object detection has witnessed significant progress by relying on large, manually annotated datasets. Annotating such datasets is highly time consuming and expensive, which motivat…

cs.CV20203 cited

Wavelet-Based Dual-Branch Network for Image Demoireing

Lin Liu, Jianzhuang Liu, Shanxin Yuan +4

When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality. In this paper, we design a wavelet-based dual-b…

cs.CV20203 cited

NTIRE 2020 Challenge on Image Demoireing: Methods and Results

Shanxin Yuan, Radu Timofte, Ales Leonardis +43

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demo…

cs.CV2020

Image Demoireing with Learnable Bandpass Filters

Bolun Zheng, Shanxin Yuan, Gregory Slabaugh +1

Image demoireing is a multi-faceted image restoration task involving both texture and color restoration. In this paper, we propose a novel multiscale bandpass convolutional neural…

cs.CV2020

Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem

Matthias De Lange, Xu Jia, Sarah Parisot +3

This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We consider the practical scenario where a high…

cs.CV2020

G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features

Wei Chen, Xi Jia, Hyung Jin Chang +2

In this paper, we propose a novel real-time 6D object pose estimation framework, named G2L-Net. Our network operates on point clouds from RGB-D detection in a divide-and-conquer fa…