HAI Weekly Seminar with Leonidas Guibas
Joint Learning Over Visual and Geometric Data
Many challenges remain in applying machine learning to domains where obtaining massive annotated data is difficult. We discuss a number of approaches that aim to reduce supervision load for learning algorithms in the visual and geometric domains by leveraging correlations among data, among representations, and among learning tasks -- what we call joint learning. The basic notion is that inference problems do not occur in isolation but rather in a social context that can be exploited to provide self-supervision by enforcing consistency among them, thus improving performance and increasing sample efficiency. An example is shape co-segmentation, where we can use structural correlations between related shapes to regularize the segmentation of any particular shape. Another is the use of cross-task consistency constraints, as in the case of inferring depth and normals from an image, which are obviously related. Even at the level of representations, joint learning can avoid blind-spots of any one individual representation and better adapt to data particularities – just as we get with multiple 2D views of a 3D object. The talk will present a number of examples of joint learning, including the above as well as 3D object detection and pose estimation.
Paul Pigott Professor of Computer Science; Professor, by courtesy, of Electrical Engineering