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Semantic Segmentation

During 2013 to 2016, I worked a lot on semantic segmentation where goal was to understanding the semantic meaning of each pixel in an image, including its object class and depth value. Over the years, we have developed several semantic segmentation algorithms.

Our CASENet algorithm demo (CVPR 2017):

Our GMRFNet algorithm demo (CVPR 2016):

Our Layered Interpretation algorithm demo (RSS 2015, NIPS 2014):

The related publications from this line of research is listed below.

  • CASENet: Deep Category-Aware Semantic Edge Detection
    Zhiding Yu, Chen Feng, Ming-Yu Liu, Srikumar Ramalingam
    Conference on Computer Vision and Pattern Recognition (CVPR), 2017, Honolulu, Hawaii,

  • Gaussian Conditional Random Field Network for Semantic Segmentation
    Raviteja Vemulapalli, Oncel Tuzel, Ming-Yu Liu, Rama Chellappa
    Conference on Computer Vision and Pattern Recognition (CVPR) (Spotlight), 2016, Las Vegas, Nevada,

  • Layered Interpretation of Street View Images
    Ming-Yu Liu, Shuoxin Lin, Srikumar Ramalingam, Oncel Tuzel
    Robotics: Science and Systems Conference (RSS) (Best paper finalist), 2015, Rome, Italy

  • Recursive Context Propagation Network for Semantic Scene Labeling
    Abhishek Sharma, Oncel Tuzel, Ming-Yu Liu
    Neural Information Processing Systems (NIPS), 2014, Montreal, Canada