activity
20162020
most citedAcquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning

3 citations · 7 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

9 papers · 1 filter

cs.CV20201 cited

CariMe: Unpaired Caricature Generation with Multiple Exaggerations

Zheng Gu, Chuanqi Dong, Jing Huo +2

Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from the…

cs.CV2020

Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning

Jinglin Xu, Wenbin Li, Jiantao Shen +5

As a concrete application of multi-view learning, multi-view classification improves the traditional classification methods significantly by integrating various views optimally. Al…

cs.CV2020

RGBD-Dog: Predicting Canine Pose from RGBD Sensors

Sinead Kearney, Wenbin Li, Martin Parsons +2

The automatic extraction of animal \reb{3D} pose from images without markers is of interest in a range of scientific fields. Most work to date predicts animal pose from RGB images,…

cs.CV2018

InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset

Wenbin Li, Sajad Saeedi, John McCormac +6

Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Loc…

cs.CV2018

Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions

M. Wagner, H. Basevi, R. Shetty +4

In-depth scene descriptions and question answering tasks have greatly increased the scope of today's definition of scene understanding. While such tasks are in principle open ended…

cs.CV2016

To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction

Wenbin Li, Seyedmajid Azimi, Aleš Leonardis +1

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing nov…