Antonio Torralba
"Understanding visual scenes"
AbstractHuman 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. As the field of computer vision 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 our 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.