49 citations · 114 across the 18 of their papers we have counts for
6 papers · 1 filter
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Huan Fu, Shunming Li, Rongfei Jia +3
Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database. The common routine is to map 2D images and 3D shap…
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…
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…
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Xiaobo Xia, Tongliang Liu, Bo Han +6
Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise. Note that there are psychological and physiological evi…
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…
Domain Adaptation as a Problem of Inference on Graphical Models
Kun Zhang, Mingming Gong, Petar Stojanov +3
This paper is concerned with data-driven unsupervised domain adaptation, where it is unknown in advance how the joint distribution changes across domains, i.e., what factors or mod…