Learning to see the physical world

Human intelligence is beyond pattern recognition. From a single image, we are able to explain what we see, reconstruct the scene in 3D, predict what's going to happen, and plan our actions accordingly. Artificial intelligence, in particular deep learning, still falls short in some preeminent aspects when compared with human intelligence, despite its phenomenal development in the past decade: they in general tackle specific problems, require large amounts of training data, and easily break when generalizing to new tasks or environments. In this dissertation, we study the problem of physical scene understanding-building versatile, data-efficient, and generalizable machines that learn to see, reason about, and interact with the physical world. The core idea is to exploit the generic, causal structure behind the world, including knowledge from computer graphics, physics, and language, in the form of approximate simulation engines, and to integrate them with deep learning.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Wu, Jiajun,Ph.D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• William T. Freeman and Joshua B. Tenenbaum.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/128332

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/128332