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Found 15 papers, 3 papers with code
L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects
In this paper, we propose a language agent with chain-of-3D-thoughts (L3GO), an inference-time approach that can reason about part-based 3D mesh generation of unconventional objects that current data-driven diffusion models struggle with.
How does the primate brain combine generative and discriminative computations in vision?
The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes giving rise to it.
Evaluating Spatial Understanding of Large Language Models
Large language models (LLMs) show remarkable capabilities across a variety of tasks.
From task structures to world models: What do LLMs know?
In what sense does a large language model have knowledge?
From seeing to remembering: Images with harder-to-reconstruct representations leave stronger memory traces
Much of what we remember is not due to intentional selection, but simply a by-product of perceiving.
3D Shape Perception Integrates Intuitive Physics and Analysis-by-Synthesis
Many surface cues support three-dimensional shape perception, but people can sometimes still see shape when these features are missing -- in extreme cases, even when an object is completely occluded, as when covered with a draped cloth.
When are Lemons Purple? The Concept Association Bias of Vision-Language Models
Large-scale vision-language models such as CLIP have shown impressive performance on zero-shot image classification and image-to-text retrieval.
Exploiting 3D Shape Bias towards Robust Vision
To tackle the problem from a new perspective, we encourage closer collaboration between the robustness and 3D vision communities.
Geon3D: Exploiting 3D Shape Bias towards Building Robust Machine Vision
To tackle the problem from a new perspective, we encourage closer collaboration between the robustness and 3D vision communities.
Explaining intuitive difficulty judgments by modeling physical effort and risk
The ability to estimate task difficulty is critical for many real-world decisions such as setting appropriate goals for ourselves or appreciating others' accomplishments.
Modeling human intuitions about liquid flow with particle-based simulation
Humans can easily describe, imagine, and, crucially, predict a wide variety of behaviors of liquids--splashing, squirting, gushing, sloshing, soaking, dripping, draining, trickling, pooling, and pouring--despite tremendous variability in their material and dynamical properties.
Self-Supervised Intrinsic Image Decomposition
Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data.
Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints
In Experiment~2, we asked participants online to judge whether they think the person in the lab used one or two hands.
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning
Humans demonstrate remarkable abilities to predict physical events in dynamic scenes, and to infer the physical properties of objects from static images.
A Concept Learning Approach to Multisensory Object Perception
This paper presents a computational model of concept learning using Bayesian inference for a grammatically structured hypothesis space, and test the model on multisensory (visual and haptics) recognition of 3D objects.
