137 citations · 464 across the 9 of their papers we have counts for
9 papers
Unifying (Machine) Vision via Counterfactual World Modeling
Daniel M. Bear, Kevin Feigelis, Honglin Chen +5
Leading approaches in machine vision employ different architectures for different tasks, trained on costly task-specific labeled datasets. This complexity has held back progress in…
Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties
Hsiao-Yu Tung, Mingyu Ding, Zhenfang Chen +6
General physical scene understanding requires more than simply localizing and recognizing objects -- it requires knowledge that objects can have different latent properties (e.g.,…
Unsupervised Segmentation in Real-World Images via Spelke Object Inference
Honglin Chen, Rahul Venkatesh, Yoni Friedman +4
Self-supervised, category-agnostic segmentation of real-world images is a challenging open problem in computer vision. Here, we show how to learn static grouping priors from motion…
Physion: Evaluating Physical Prediction from Vision in Humans and Machines
Daniel M. Bear, Elias Wang, Damian Mrowca +12
While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion,…
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
Chuang Gan, Jeremy Schwartz, Seth Alter +21
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between m…
Learning Physical Graph Representations from Visual Scenes
Daniel M. Bear, Chaofei Fan, Damian Mrowca +8
Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization. However, CNNs do not explicitly encode objects, parts, an…