Teaching computers to understand the visual world
We want to change the way you interact with visual data. We want to make your photos magical, we want to deeply understand images and videos from cameras everywhere: in your phone, on your Xbox, in your fridge, on robots, in cars, anywhere. We want you to be able to find your stuff, answer questions, make fantastic new images. And we do that by inventing new algorithms and thinking of new mathematical models for how images come to be.
Mingsong Dou, Sameh Khamis, Yury Degtyarev, Philip Davidson, Sean Fanello, Adarsh Kowdle, Sergio Orts Escolano, Christoph Rhemann, David Kim, Jonathan Taylor, Pushmeet Kohli, Vladimir Tankovich, and Shahram Izadi, Fusion4D: Real-time Performance Capture of Challenging Scenes, SIGGRAPH, July 2016.
Y. Lewenberg, Y. Bachrach, S. Shankar, and A. Criminisi, Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information, in Intl Joint Conference on Artifical Intelligence (IJCAI), July 2016.
Jaesik Park, Yu-Wing Tai, Sudipta N Sinha, and In So Kweon, Efficient and Robust Color Consistency for Community Photo Collections, in Computer Vision and Pattern Recognition (CVPR), IEEE – Institute of Electrical and Electronics Engineers, 26 June 2016.
Tatsunori Taniai, Sudipta N Sinha, and Yoichi Sato, Joint Recovery of Dense Correspondence and Cosegmentation in Two Images, IEEE – Institute of Electrical and Electronics Engineers, 26 June 2016.
Kenneth Tran, Xiaodong He, Lei Zhang, Jian Sun, Cornelia Carapcea, Chris Thrasher, Chris Buehler, and Chris Sienkiewicz, Rich Image Captioning in the Wild, in Proceedings of CVPR 2016, June 2016.
Join us! Do you love to turn mathematics into code? Do you want to build the future? Then Apply here.
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- Video Analytics
- Acquisition, Reconstruction, Transmission, and Display of Live 3D Content
- Image Chat
- Dog Recognition
- Large Scale Weakly Supervised Learning
- Photo Story
- Network Morphism
- Food Recognition
Our research page.