activity
20172020
most citedCausal Discovery in the Presence of Measurement Error: Identifiability Conditions

20 citations · 53 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CV202016 cited

3D-FUTURE: 3D Furniture shape with TextURE

Huan Fu, Rongfei Jia, Lin Gao +4

The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories. Thus, they typically have insufficient geometric details and less informative textur…

cs.CV20202 cited

Short-Term and Long-Term Context Aggregation Network for Video Inpainting

Ang Li, Shanshan Zhao, Xingjun Ma +5

Video inpainting aims to restore missing regions of a video and has many applications such as video editing and object removal. However, existing methods either suffer from inaccur…

cs.LG20205 cited

Multi-Class Classification from Noisy-Similarity-Labeled Data

Songhua Wu, Xiaobo Xia, Tongliang Liu +5

A similarity label indicates whether two instances belong to the same class while a class label shows the class of the instance. Without class labels, a multi-class classifier coul…

stat.ML2019

Likelihood-Free Overcomplete ICA and Applications in Causal Discovery

Chenwei Ding, Mingming Gong, Kun Zhang +1

Causal discovery witnessed significant progress over the past decades. In particular, many recent causal discovery methods make use of independent, non-Gaussian noise to achieve id…

cs.CV2019

Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach

Yipeng Mou, Mingming Gong, Huan Fu +3

Majority of state-of-the-art monocular depth estimation methods are supervised learning approaches. The success of such approaches heavily depends on the high-quality depth labels…

cs.LG2019

Twin Auxiliary Classifiers GAN

Mingming Gong, Yanwu Xu, Chunyuan Li +2

Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly…