no code implementations • CVPR 2016 • Shaojing Fan, Tian-Tsong Ng, Bryan L. Koenig, Ming Jiang, Qi Zhao
(3) It can guide the design of a generalized computational algorithm for multi-dimensional visual perception.
2 code implementations • CVPR 2016 • Amir Shahroudy, Jun Liu, Tian-Tsong Ng, Gang Wang
Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes.
no code implementations • 23 Mar 2016 • Amir Shahroudy, Tian-Tsong Ng, Yihong Gong, Gang Wang
Single modality action recognition on RGB or depth sequences has been extensively explored recently.
no code implementations • ICCV 2015 • Siying Liu, Tian-Tsong Ng, Kalyan Sunkavalli, Minh N. Do, Eli Shechtman, Nathan Carr
In this work, we investigate the problem of automatically inferring the lattice structure of near-regular textures (NRT) in real-world images.
no code implementations • 17 Nov 2015 • Jinghua Wang, Abrar Abdul Nabi, Gang Wang, Chengde Wan, Tian-Tsong Ng
Given attributes as representations, we propose to learn a ranking SPN (sum product networks) to rank pairs of fashion images.
no code implementations • 31 Jul 2015 • Amir Shahroudy, Gang Wang, Tian-Tsong Ng, Qingxiong Yang
We propose a joint sparse regression based learning method which utilizes the structured sparsity to model each action as a combination of multimodal features from a sparse set of body parts.
no code implementations • CVPR 2014 • Shaojing Fan, Tian-Tsong Ng, Jonathan S. Herberg, Bryan L. Koenig, Cheston Y.-C. Tan, Rangding Wang
In this paper we systematically evaluate factors underlying human perception of visual realism and use that information to create an automated assessment of visual realism.