activity
20222026
most citedDrawing out of Distribution with Neuro-Symbolic Generative Models

4 citations · 11 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes

Ruihong Shen, Žiga Kovačič, Peter Kulits +6

We introduce 4DCodeBench, a benchmark for 4D inverse graphics through code generation, in which agents reconstruct dynamic scenes from video as executable graphics programs. To acc…

q-bio.NC2024★ 1 cited

Vision CNNs trained to estimate spatial latents learned similar ventral-stream-aligned representations

Yudi Xie, Weichen Huang, Esther Alter +3

Studies of the functional role of the primate ventral visual stream have traditionally focused on object categorization, often ignoring -- despite much prior evidence -- its role i…

cs.CV2024★ 1 cited

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…

cs.CV2024

What Makes a Maze Look Like a Maze?

Joy Hsu, Jiayuan Mao, Joshua B. Tenenbaum +2

A unique aspect of human visual understanding is the ability to flexibly interpret abstract concepts: acquiring lifted rules explaining what they symbolize, grounding them across f…

q-bio.NC2024★ 3 cited

How does the primate brain combine generative and discriminative computations in vision?

Benjamin Peters, James J. DiCarlo, Todd Gureckis +10

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as we…

cs.CV2023★ 2 cited

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…

cs.LG2022★ 4 cited

Drawing out of Distribution with Neuro-Symbolic Generative Models

Yichao Liang, Joshua B. Tenenbaum, Tuan Anh Le +1

Learning general-purpose representations from perceptual inputs is a hallmark of human intelligence. For example, people can write out numbers or characters, or even draw doodles,…