Object-Oriented Learning (OOL): Perception, Representation, and Reasoning

International Conference on Machine Learning (ICML)

Friday July 17, 2020, Virtual Workshop

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Implicit Neural Scene Representations

  • Vincent Sitzmann
  • (Invited Talk)

The video will become available after 1st August 2020 in accordance with the ICML2020 Code of Conduct.

How we represent signals has major implications for the algorithms we build to analyze them. Today, most signals are represented discretely: Images as grids of pixels, shapes as point clouds, audio as grids of amplitudes, etc. If images weren't pixel grids - would we be using convolutional neural networks today? What makes a good or bad representation? Can we do better? I will talk about leveraging emerging implicit neural representations for complex & large signals, such as room-scale geometry, images, audio, video, and physical signals defined via partial differential equations. By embedding an implicit scene representation in a neural rendering framework and learning a prior over these representations, I will show how we can enable 3D reconstruction from only a single posed 2D image. Finally, I will show how gradient-based meta-learning can enable fast inference of implicit representations, and how the features we learn in the process are already useful to the downstream task of semantic segmentation.