Object-Oriented Learning (OOL): Perception, Representation, and Reasoning
International Conference on Machine Learning (ICML)
Friday July 17, 2020, Virtual Workshop
The video will become available after 1st August 2020 in accordance with the ICML2020 Code of Conduct.
We propose a novel approach to representation learning based on object keypoints. It leverages the predictability of local image regions from spatial neighborhoods to identify salient regions that correspond to object parts, which are then converted to keypoints. Unlike prior approaches, this does not overly bias the keypoints to focus on a particular property of objects. We demonstrate the efficacy of our approach on Atari where we find that it learns keypoints corresponding to the most salient object parts and is more robust to certain visual distractors.