GRASP Special Seminar: Björn Ommer, Heidelberg University, "Towards Explainable Models for Understanding our (Visual) World"

ABSTRACT

The ultimate goal of computer vision are models that understand our (visual) world. Explainable AI extends this further, seeking models whose decisions can in addition also be understood by a human user. Such image and video understanding is a difficult inverse problem. It requires learning a metric in image space that reflects object relations in real world. To avoid the need for tedious annotations, we follow a self-supervised strategy to metric and representation learning. We present a divide-and-conquer approach to representation learning that exploits transitivity to discover reliable relationships for training. In addition to that, the talk will present a widely applicable strategy based on deep reinforcement learning to improve the surrogate tasks underlying self-supervision.

Thereafter, we will discuss the learning of explainable models by disentangling representations into diverse object characteristics. This yields a generative model for image and video synthesis, controlled visual retargeting, and unsupervised learning of semantic parts and registration. Time permitting, we can also cover a variety of applications of this research ranging from behavior analysis in neuroscience to visual analytics in the digital humanities.

 

Presenter's biography

Björn Ommer is a professor in the department of mathematics and computer science at Heidelberg University and heading the Computer Vision Group. He received his diploma in computer science from University of Bonn and his PhD from ETH Zurich. Thereafter, he was a postdoc in the vision group of Jitendra Malik at UC Berkeley.

Björn serves as an associate editor for IEEE T-PAMI and previously for Pattern Recognition Letters. He is a co-director of the Interdisciplinary Center for Scientific Computing (IWR) and the Heidelberg Collaboratory for Image Processing. His research interests include semantic scene understanding, visual retrieval and synthesis, self-supervised metric and representation learning, and explainable AI. Moreover, he is applying this basic research in interdisciplinary projects with neuroscience and the digital humanities for which he also received an additional cooptation in the department of philosophy.