David Novotny, Research Scientist at Facebook AI Research, London, UK
Dense image labeling for image matching and instance segmentation

Abstract:
A learnable component that lifts image pixels into a high-dimensional space is an integral part of any modern image recognition system. In this talk, I will present two deep architectures that achieve improved results predominantly due to a careful design of this pixel-embedding step.
The first part of the talk presents a self-supervised architecture that produces pixel-wise descriptors for establishing image-to-image correspondences. The key ingredient is a novel probabilistic introspection learning scheme which filters out unimportant background samples, allowing the network to selectively represent image pixels that have the potential to result in a correct match.
Next, the task of grouping image pixels belonging to an object is addressed. More specifically, we deal with the instance segmentation problem using a deep convolutional architecture that “colors” image pixels with their instance labels. Identifying the convolutional coloring dilemma, a drawback of standard position-agnostic networks that prevents them from solving this task, we propose a simple correction comprising a novel position-sensitive semi-convolutional operator.

Speaker:
David Novotny is a Research Scientist at Facebook AI Research, London, UK. Previously, he was a DPhil student in the VGG group, University of Oxford in collaboration with Naver Labs Europe, supervised by Dr. Diane Larlus. and Prof. Andrea Vedaldi. While working as a researcher at CMP Prague under the supervision of Prof. Jiri Matas, he studied at the Czech Technical University and received his MS degree (with honours) in computer vision and machine learning in 2015. His current research interests are weakly supervised representation learning, matching, single-view 3D reconstruction, pose estimation and instance segmentation.

Website Article: http://bit.ly/NLESeminar0802