Liang-Chieh (Jay) Chen- Home Page

jay photo 

Liang-Chieh Chen
Google LA

Email: lcchen at cs dot ucla dot edu

About me

Since February 2016, I have been working at Google, Los Angeles.

I finished my Ph.D. in Computer Science at UCLA under the supervision of Alan L. Yuille in November 2015. My research interests include Computer Vision, Graphical Models, and Machine Learning.

News

Education

Papers

decoder 

The Devil is in the Decoder
Zbigniew Wojna, Vittorio Ferrari, Sergio Guadarrama, Nathan Silberman, Liang-Chieh Chen, Alireza Fathi, and Jasper Uijlings
In British Machine Vision Conference (BMVC), Imperial College London, September 2017.
[pdf (arxiv: 1707.05847)]

deeplabv3 

Rethinking Atrous Convolution for Semantic Image Segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam
Technical report
[preprint (arxiv: 1706.05587)]

aspp 

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Liang-Chieh Chen*, George Papandreou*, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille (*equal contribution)
Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
[preprint (arxiv: 1606.00915)] [project] [code]

hazn 

Zoom Better to See Clearer: Human Part Segmentation with Auto Zoom Net
Fangting Xia, Peng Wang, Liang-Chieh Chen, and Alan L. Yuille
In European Conference in Computer Vision (ECCV), Amsterdam, Netherlands, October 2016.
[preprint (arxiv: 1511.06881)]

deeplab_dt 

Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, and Alan L. Yuille
In Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, USA, June 2016.
[preprint (arxiv: 1511.03328)] [project] [code]

deeplab_attention 

Attention to Scale: Scale-aware Semantic Image Segmentation
Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, and Alan L. Yuille
In Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, USA, June 2016.
[preprint (arxiv: 1511.03339)] [Test results on subset of MPII] [project] [code]

abc_cnn 

ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
Kan Chen, Jiang Wang, Liang-Chieh Chen, Haoyuan Gao, Wei Xu, and Ram Nevatia
Technical Report
[preprint (arxiv: 1511.05960)]

deeplab_semi 

Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
George Papandreou*, Liang-Chieh Chen*, Kevin Murphy, and Alan L. Yuille
(*equal contribution)
International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015.
[preprint (arxiv: 1502.02734)] [spotlight] [project] [code]

DeepStructured 

Learning Deep Structured Models
Liang-Chieh Chen*, Alexander G. Schwing*, Alan L. Yuille, and Raquel Urtasun
(*equal contribution)
International Conference on Machine Learning (ICML), Lille, France, July 2015.
[pdf] [talk]

deeplab 

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Liang-Chieh Chen*, George Papandreou*, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille
(*equal contribution)
International Conference on Learning Representations (ICLR), San Diego, California, USA, May 2015.
[pdf (arXiv:1412.7062)] [project] [code]

SegKITTI 

Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision
Liang-Chieh Chen, Sanja Fidler, Alan L. Yuille, and Raquel Urtasun
In Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 2014.
[pdf] [Supplementary Doc] [project] [CAD models]

Mini-epitomes 

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes
George Papandreou, Liang-Chieh Chen, and Alan L. Yuille
In Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 2014.
[pdf] [Supplementary Doc]

Shape epitome CRF 

Learning a Dictionary of Shape Epitomes with Applications to Image Labeling
Liang-Chieh Chen, George Papandreou, and Alan L. Yuille
In International Conference on Computer Vision (ICCV), Sydney, Australia, Dec. 2013.
[pdf] [Supplementary Doc]

US Patents