1 citations · 1 across the 14 of their papers we have counts for
6 papers · 1 filter
-0.6: Adaptive Streams of Thought in a Unified Vision-Language World Model
Khai Loong Aw, Klemen Kotar, Baihan Zhang +5
Humans reason about the physical world by flexibly combining different forms of inference as a problem unfolds. Achieving this flexibility is a major open challenge in AI: capabili…
Scaling cross-situational word learning on children's naturalistic experiences
Michael C. Frank, Alvin W. M. Tan, Khai Loong Aw +9
How do children learn the meanings of the simple, concrete words that form a majority of their early vocabulary? Statistical information about many word meanings can be recovered f…
Perceptual 3D Simulation With Physical World Modeling
Wanhee Lee, Klemen Kotar, Rahul Mysore Venkatesh +2
Predicting how a scene will evolve after a desired 3D transformation from images is a central goal in vision, graphics, and robotics. Yet unlike ideal simulators with full access t…
Physical Object Understanding with a Physically Controllable World Model
Rahul Venkatesh, Klemen Kotar, Lilian Naing Chen +9
A central challenge in visual intelligence is learning the physical structure of scenes from raw videos: how regions form objects and the laws that govern their interactions. Solvi…
Unified 3D Scene Understanding Through Physical World Modeling
Wanhee Lee, Klemen Kotar, Rahul Mysore Venkatesh +4
Understanding 3D scenes requires flexible combinations of visual reasoning tasks, including depth estimation, novel view synthesis, and object manipulation, all of which are essent…
Zero-shot World Models Are Developmentally Efficient Learners
Khai Loong Aw, Klemen Kotar, Wanhee Lee +6
Young children demonstrate early abilities to understand their physical world, estimating depth, motion, object coherence, interactions, and many other aspects of physical scene un…