Physical scene understanding

Current AI systems still fail to match the flexibility, robustness, and generalizability of human intelligence: how even a young child can manipulate objects to achieve goals of their own invention or in cooperation, or can learn the essentials of a complex new task within minutes. We need AI with s...

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Bibliographic Details
Published in:The AI magazine 2024-03, Vol.45 (1), p.156-164
Main Author: Wu, Jiajun
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Communication
Computer science
Engines
Geometry
Human performance
Intelligence
Neurosciences
Physical properties
Physics
Probabilistic inference
Robotics
Scene analysis
Semantics
Simulation
Sound
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Summary:Current AI systems still fail to match the flexibility, robustness, and generalizability of human intelligence: how even a young child can manipulate objects to achieve goals of their own invention or in cooperation, or can learn the essentials of a complex new task within minutes. We need AI with such embodied intelligence: transforming raw sensory inputs to rapidly build a rich understanding of the world for seeing, finding, and constructing things, achieving goals, and communicating with others. This problem of physical scene understanding is challenging because it requires a holistic interpretation of scenes, objects, and humans, including their geometry, physics, functionality, semantics, and modes of interaction, building upon studies across vision, learning, graphics, robotics, and AI. My research aims to address this problem by integrating bottom‐up recognition models, deep networks, and inference algorithms with top‐down structured graphical models, simulation engines, and probabilistic programs.
ISSN:0738-4602
2371-9621
DOI:10.1002/aaai.12148