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Found 10 papers, 6 papers with code
Divergences between Language Models and Human Brains
In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories.
Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models
Pre-trained and frozen large language models (LLMs) can effectively map simple scene rearrangement instructions to programs over a robot's visuomotor functions through appropriate few-shot example prompting.
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity
Our results show that BrainSCUBA is a promising means for understanding functional preferences in the brain, and provides motivation for further hypothesis-driven investigation of visual cortex.
Thinking Like an Annotator: Generation of Dataset Labeling Instructions
In Labeling Instruction Generation, we take a reasonably annotated dataset and: 1) generate a set of examples that are visually representative of each category in the dataset; 2) provide a text label that corresponds to each of the examples.
Quantifying the Roles of Visual, Linguistic, and Visual-Linguistic Complexity in Verb Acquisition
Based on these results, we conclude that verb acquisition is influenced by all three sources of complexity, but that the variability of visual structure poses the most significant challenge for verb learning.
TIDEE: Tidying Up Novel Rooms using Visuo-Semantic Commonsense Priors
We introduce TIDEE, an embodied agent that tidies up a disordered scene based on learned commonsense object placement and room arrangement priors.
Learning Neural Acoustic Fields
By modeling acoustic propagation in a scene as a linear time-invariant system, NAFs learn to continuously map all emitter and listener location pairs to a neural impulse response function that can then be applied to arbitrary sounds.
AlphaNet: Improving Long-Tail Classification By Combining Classifiers
Methods in long-tail learning focus on improving performance for data-poor (rare) classes; however, performance for such classes remains much lower than performance for more data-rich (frequent) classes.
Learning Intermediate Features of Object Affordances with a Convolutional Neural Network
Our ability to interact with the world around us relies on being able to infer what actions objects afford -- often referred to as affordances.
BOLD5000: A public fMRI dataset of 5000 images
Vision science, particularly machine vision, has been revolutionized by introducing large-scale image datasets and statistical learning approaches.
