Neural scene de-rendering
We study the problem of holistic scene understanding. We would like to obtain a compact, expressive, and interpretable representation of scenes that encodes information such as the number of objects and their categories, poses, positions, etc. Such a representation would allow us to reason about and...
Main Authors: | Wu, Jiajun, Tenenbaum, Joshua B, Kohli, Pushmeet |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2020
|
Online Access: | https://hdl.handle.net/1721.1/126659 |
Similar Items
-
The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
by: Mao, Jiayuan, et al.
Published: (2020) -
Learning to see physics via visual de-animation
by: Wu, Jiajun, et al.
Published: (2021) -
Picture: A Probabilistic Programming Language for Scene Perception
by: Kulkarni, Tejas Dattatraya, et al.
Published: (2015) -
End-to-End Optimization of Scene Layout
by: Luo, Andrew, et al.
Published: (2021) -
Neurocomputational Modeling of Human Physical Scene Understanding
by: Yildirim, Ilker, et al.
Published: (2020)