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Abhinav Gupta
Assistant Research Professor, RI
Email:
Office: EDSH 213
Phone: (412) 268-2067
  Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Ave
Pittsburgh, PA 15213
Administrative Assistant: Lynnetta J. Miller
Personal Homepage

News and Media
 
Computer Searches Web 24/7 To Analyze Images and Teach Itself Common Sense
November 20, 2013. A computer program called the Never Ending Image Learner (NEIL) is running 24 hours a day at Carnegie Mellon University, searching the Web for images, doing its best to understand them on its own and, as it builds a growing visual database, gathering common sense on a massive scale.
The Look of Paris: Visual Data Mining of Google Street View
August 07, 2012. Paris is one of those cities that has a look all its own, something that goes beyond landmarks such as the Eiffel Tower or Notre Dame. Researchers at Carnegie Mellon University and INRIA/Ecole Normale Supérieure in Paris have developed visual data mining software that can automatically detect these sometimes subtle features, such as street signs, streetlamps and balcony railings, that give Paris and other cities a distinctive look.
Robotics Institute Creates Method for Cross-Domain Image Matching
December 06, 2011. Computers can mimic the human ability to find visually similar images, such as photographs of a fountain in summer and in winter, or a photograph and a painting of the same cathedral, by using a technique that analyzes the uniqueness of images, say researchers at Carnegie Mellon University’s School of Computer Science. The research team found that its surprisingly simple technique performed well on a number of visual tasks that normally stump computers, including matching sketches of automobiles with photographs of cars.
Block-Based Method Helps Computers Decipher Outdoor Scenes
September 09, 2010. Computer vision systems can struggle to make sense of a single image, but a new method devised by computer scientists at Carnegie Mellon University enables computers to gain a deeper understanding of an image by reasoning about the physical constraints of the scene.