6 citations · 12 across the 5 of their papers we have counts for
4 papers · 1 filter
GenMatter: Perceiving Physical Objects with Generative Matter Models
Eric Li, Arijit Dasgupta, Yoni Friedman +5
Human visual perception offers valuable insights for understanding computational principles of motion-based scene interpretation. Humans robustly detect and segment moving entities…
Evaluating Multiview Object Consistency in Humans and Image Models
Tyler Bonnen, Stephanie Fu, Yutong Bai +5
We introduce a benchmark to directly evaluate the alignment between human observers and vision models on a 3D shape inference task. We leverage an experimental design from the cogn…
Approaching human 3D shape perception with neurally mappable models
Thomas P. O'Connell, Tyler Bonnen, Yoni Friedman +4
Humans effortlessly infer the 3D shape of objects. What computations underlie this ability? Although various computational models have been proposed, none of them capture the human…
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…