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GRASP Seminar Series: Fall 2009
September 18th, 11:00 a.m., Wu & Chen Auditorium, Levine Hall (3330 Walnut Street)
Antonio Torralba
Massachusetts Institute of Technology
"Understanding Visual Scenes"
Abstract: Human visual scene understanding is remarkable: with only a brief glance at an image, an abundance of information is available - spatial structure, scene category and the identity of main objects in the scene. In traditional computer vision, scene and object recognition are two visual tasks generally studied separately. However, it is unclear whether it is possible to build robust systems for scene and object recognition, matching human performance, based only on local representations. Another key component of machine vision algorithms is the access to data that describe the content of images. As the field moves into integrated systems that try to recognize many object classes and learn about contextual relationships between objects, the lack of large annotated datasets hinders the fast development of robust solutions. In the early days, the first challenge a computer vision researcher would encounter would be the difficult task of digitizing a photograph. Even once a picture was in digital form, storing a large number of pictures (say six) consumed most of the available computational resources. In addition to the algorithmic advances required to solve object recognition, a key component to progress is access to data in order to train computational models for the different object classes. This situation has dramatically changed in the last decade, especially via the internet, which has given computer vision researchers access to billions of images and videos. In this talk I will describe recent work on visual scene understanding that try to build integrated models for scene and object recognition, emphasizing the power of large database of annotated images in computer vision.
Biography: Antonio Torralba is associate Professor of Electrical Engineering and Computer Science at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Following his degree in telecommunications engineering, obtained at the Universidad Politecnica de Catalunya, Spain, he was awarded a Ph.D. in Signal, Image, and Speech processing from the Institut National Polytechnique de Grenoble, France. Thereafter, he spent post-doctoral training at the Brain and Cognitive Science Department and the Computer Science and Artificial Intelligence Laboratory at MIT.
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