Speaker: Prof. Alan L. Yuille

Abstract:

Big data arises naturally in computer vision because of the enormous number and variety of images and the large range of visual tasks that we want to perform on them. Computer vision researchers must pay increasingly attention to complexity issues as they develop algorithms that work on large image datasets. This talk has two parts. The first part describes practical issues that arise when working with large datasets such as Pascal and ImageNet. These include efficient algorithms, parallel implementations (e.g., GPUs), and special purpose hardware. The second part describes theoretical work that addresses arguably the fundamental problem of vision — how can a visual system store (represent), rapidly access (do inference), and learn the enormous number and variety of objects — and configurations of objects — that occur in the world? We propose and analysis a simplified hierarchical compositional model that can address many of these issues, and which may relate to the structure of the human visual system.