Faculty and students from the Center for Visual Computing will present 17 papers at CVPR 2017

Faculty and students from the Center for Visual Computing will present 12 papers at CVPR 2017, the premier international forum for computer vision research this year, held in Honolulu, Hawaii.

1. Robust Energy Minimization for BRDF-Invariant Shape From Light Fields
Zhengqin Li, Zexiang Xu, Ravi Ramamoorthi, Manmohan Chandraker

2. Light Field Blind Motion Deblurring
Pratul P. Srinivasan, Ren Ng, Ravi Ramamoorthi

3. Deeply Supervised Salient Object Detection With Short Connections
Qibin Hou, Ming-Ming Cheng, Xiaowei Hu, Ali Borji, Zhuowen Tu, Philip H. S. Torr

4. Aggregated Residual Transformations for Deep Neural Networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He

5. Semantically Consistent Regularization for Zero-Shot Recognition
Pedro Morgado, Nuno Vasconcelos

6. AGA: Attribute-Guided Augmentation
Mandar Dixit, Roland Kwitt, Marc Niethammer, Nuno Vasconcelos

7. Deep Learning With Low Precision by Half-Wave Gaussian Quantization
Zhaowei Cai, Xiaodong He, Jian Sun, Nuno Vasconcelos

8. Deep Supervision With Shape Concepts for Occlusion-Aware 3D Object Parsing
Chi Li, M. Zeeshan Zia, Quoc-Huy Tran, Xiang Yu, Gregory D. Hager, Manmohan Chandraker

9. DESIRE: Distant Future Prediction in Dynamic Scenes With Interacting Agents
Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B. Choy, Philip H. S. Torr, Manmohan Chandraker

10. Deep Network Flow for Multi-Object Tracking
Samuel Schulter, Paul Vernaza, Wongun Choi, Manmohan Chandraker

11. Learning Random-Walk Label Propagation for Weakly-Supervised Semantic Segmentation
Paul Vernaza, Manmohan Chandraker

12. Person Re-Identification in the Wild
Liang Zheng, Hengheng Zhang, Shaoyan Sun, Manmohan Chandraker, Yi Yang, Qi Tian

13. A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Hao Su, Haoqiang Fan and Leonidas Guibas.

14. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Hao Su, Charles Qi, Kaichun Mo and Leonidas Guibas.

15. SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation 
Li Yi, Hao Su, Xingwen Guo and Leonidas Guibas.

16. Learning Shape Abstractions by Assembling Volumetric Primitives
Shubham Tulsiani, Hao Su, Leonidas Guibas, Alexei A. Efros and
Jitendra Malik.

17. Learning Non-Lambertian Object Intrinsics across ShapeNet Categories
Jian Shi, Yue Dong, Hao Su and Stella X. Yu.

Center for Visual Computing papers at SIGGRAPH 2017

Faculty and students from the Center for Visual Computing will present five papers at SIGGRAPH 2017, the premier international forum for computer graphics research this year, held in Los Angeles.

Center for Visual Computing papers at SIGGRAPH 2017:

1. "Antialiasing Complex Global Illumination Effects in Path-space” by Laurent Belcour, Lingqi Yan, Ravi Ramamoorthi and Derek Nowrouzezahrai

2. “An Efficient and Practical Near and Far Field Fur Reflectance Model” by Lingqi Yan, Henrik Wann Jensen and Ravi Ramamoorthi

3. "Light Field Video Capture Using a Learning-Based Hybrid Imaging System” by Ting-Chun Wang, Junyan Zhu, Nima Khademi Kalantari, Alexei A. Efros and Ravi Ramamoorthi

4. “Deep High Dynamic Range Imaging of Dynamic Scenes” by Nima Khademi Kalantari and Ravi Ramamoorthi

5. "Patch-Based Optimization for Image-Based Texture Mapping” by Sai Bi, Nima Khademi Kalantari and Ravi Ramamoorthi

6. "Learning Hierarchical Shape Segmentation and Labeling from Online Repositories" by Li Yi, Leonidas J. Guibas, Aaron Hertzmann, Vladimir G. Kim, Hao Su, Ersin Yumer

For more about the Center for Visual Computing visit viscomp.ucsd.edu.

Center for Visual Computing Faculty and Students to Present 8 papers at the European Conference on Computer Vision

Faculty and students from the Center for Visual Computing will present eight papers at ECCV, the European Conference on Computer Vision, Oct. 8-16, 2016 the premier international forum for computer vision research this year, held in Amsterdam. 

Center for Visual Computing papers at ECCV 2016:

1. Top-down Learning for Structured Labeling with Convolutional Pseudoprior 
Saining Xie, Xun Huang, Zhuowen Tu

2. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Zhaowei Cai, Quanfu Fan, Rogerio Feris, Nuno Vasconcelos

3. Semantic Clustering for Robust Fine-Grained Scene Recognition 
MarianGeorge, Dixit Mandar, Gábor Zogg, Nuno Vasconcelos

4. Peak-Piloted Deep Network for Facial Expression Recognition
Xiangyun Zhao, Xiaodan Liang, Luoqi Liu, Teng Li, Yugang Han, Nuno Vasconcelos, Shuicheng Yan

5. HFS: Hierarchical Feature Selection for Efficient Image Segmentation
Ming-Ming Cheng, Yun Liu, Qibin Hou, Jiawang Bian, Philip Torr, Shimin Hu, Zhuowen Tu

6. Linear depth estimation from an uncalibrated, monocular polarisation image
William Smith, Ravi Ramamoorthi, Silvia Tozza

7. A 4D Light-Field Dataset and CNN Architectures for Material Recognition
Ting-Chun Wang, Jun-Yan Zhu, Hiroaki Ebi, Manmohan Chandraker, Alexei Efros, Ravi Ramamoorthi

8. Deep Deformation Network for Object Landmark Localization
Xiang Yu, Feng Zhou, Manmohan Chandraker

Visual Computing Center Faculty and students will also present three papers at the SIGGRAPH Asia 2016 computer graphics conference, held in Macao in early December.

Center for Visual Computing papers at SIGGRAPH Asia 2016:

1. Minimal BRDF Sampling for Two-Shot Near-Field Reflectance
Acquisition, Zexiang Xu, Jannik Boll Nielsen, Jiyang Yu, Henrik Wann Jensen, Ravi Ramamoorthi

2. Downsampling Scattering Parameters for Rendering Anisotropic Media
Shuang Zhao, Lifan Wu, Fredo Durand, Ravi Ramamoorthi

3. Learning-Based View Synthesis for Light Field Cameras
Nima Khademi Kalantari, Ting-Chun Wang, Ravi Ramamoorthi

Emotient's Marni Bartlett speaks at the RE.WORK Deep Learning Summit in 2015

Marni Bartlett, the CEO of Emotient, who also is a researcher in the Machine Perception Lab at the Qualcomm Institute here at UC San Diego, gave a talk about how her company's research meshes with Deep Learning.
The company developed software that can differentiate fake and real pain better than humans. Researchers are currently looking at how emotion can impact people's decisions and other issues.
Full talk here:  http://youtu.be/43SHi1Qjj70
Our press release here: http://www.jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=1519
Emotient website here: http://www.emotient.com/
Bonus: talk by cognitive science alum Josh Susskind, a senior data scientist at Emotient: http://youtu.be/3v-DHW75VQA

This UC San Diego researcher has had a busy week

ABC US News | ABC Business News
It's been a busy week for Marni Bartlett, a researcher at the Institute for Neural Computation here at UC San Diego. She was at a conference in Orlando, Fla., when ABC News called with an interview request. Bartlett was happy to oblige and jumped into a taxi to head out to the network's local affiliate to shoot an on-camera interview.
You can watch the resulting segment here . 
What had very likely gotten the network's attention was a story about Bartlett's research in The New York Times' Science Section April 29. The story explains how Bartlett and colleagues in the Machine Perception Lab, which is housed at Calit2, have developed a software toolbox that is able to tell whether someone is faking pain better than human observers. You can read more about the research here.
Then you can figure out how good you are a spotting people who are faking pain by taking this New York Times quiz. 
Bartlett and colleagues are getting some of these technologies ready for commercialization through a start up they launched last year, Emotient.