DeepMind's AlignNet Learns Stable Object Representations Across Time

New research from UK based AI company and research lab DeepMind is enabling AI agents to perceive dynamic real-world environments more like humans do. The work deals with aligning observed entities across time-steps in both fully observable and partially observable environments and is introduced in the paper AlignNet: Unsupervised Entity Alignment. While humans interact with the world we draw on our understanding of the objects or entities in the environment -- which remains coherent even if an object becomes temporarily occluded. AI agents however have typically been trained using only pixel inputs. Although recently developed unsupervised scene segmentation techniques have enabled object-based inputs, these approaches are limited to single frames, and models cannot keep track of how objects segmented at one time-step correspond (or align) to those at a later time-step.