7 citations · 12 across the 11 of their papers we have counts for
13 papers
Receptive-field-constrained stimulus optimization for human early and intermediate visual cortex
Junru Zhao, Hanfei Guo, Andrew Luo +1
An ongoing challenge in sensory neuroscience is to characterize the feature dimensions encoded by cortical populations. Recent approaches probe feature selectivity in a data-driven…
Human-like Object Grouping in Self-supervised Vision Transformers
Hossein Adeli, Seoyoung Ahn, Andrew Luo +3
Vision foundation models trained with self-supervised objectives achieve strong performance across diverse tasks and exhibit emergent object segmentation properties. However, their…
Real-to-Sim for Highly Cluttered Environments via Physics-Consistent Inter-Object Reasoning
Tianyi Xiang, Jiahang Cao, Sikai Guo +3
Reconstructing physically valid 3D scenes from single-view observations is a prerequisite for bridging the gap between visual perception and robotic control. However, in scenarios…
In Silico Mapping of Visual Categorical Selectivity Across the Whole Brain
Ethan Hwang, Hossein Adeli, Wenxuan Guo +2
A fine-grained account of functional selectivity in the cortex is essential for understanding how visual information is processed and represented in the brain. Classical studies us…
Vision Transformers with Self-Distilled Registers
Yinjie Chen, Zipeng Yan, Chong Zhou +2
Vision Transformers (ViTs) have emerged as the dominant architecture for visual processing tasks, demonstrating excellent scalability with increased training data and model size. H…
Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex
Muquan Yu, Mu Nan, Hossein Adeli +6
Understanding functional representations within higher visual cortex is a fundamental question in computational neuroscience. While artificial neural networks pretrained on large-s…
Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers
Andrew F. Luo, Jacob Yeung, Rushikesh Zawar +4
We introduce BrainSAIL, a method for linking neural selectivity with spatially distributed semantic visual concepts in natural scenes. BrainSAIL leverages recent advances in large-…
Disentangled Acoustic Fields For Multimodal Physical Scene Understanding
Jie Yin, Andrew Luo, Yilun Du +4
We study the problem of multimodal physical scene understanding, where an embodied agent needs to find fallen objects by inferring object properties, direction, and distance of an…
StableSemantics: A Synthetic Language-Vision Dataset of Semantic Representations in Naturalistic Images
Rushikesh Zawar, Shaurya Dewan, Andrew F. Luo +3
Understanding the semantics of visual scenes is a fundamental challenge in Computer Vision. A key aspect of this challenge is that objects sharing similar semantic meanings or func…
DiffusionPID: Interpreting Diffusion via Partial Information Decomposition
Rushikesh Zawar, Shaurya Dewan, Prakanshul Saxena +3
Text-to-image diffusion models have made significant progress in generating naturalistic images from textual inputs, and demonstrate the capacity to learn and represent complex vis…
Reanimating Images using Neural Representations of Dynamic Stimuli
Jacob Yeung, Andrew F. Luo, Gabriel Sarch +3
While computer vision models have made incredible strides in static image recognition, they still do not match human performance in tasks that require the understanding of complex,…
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity
Andrew F. Luo, Margaret M. Henderson, Michael J. Tarr +1
Understanding the functional organization of higher visual cortex is a central focus in neuroscience. Past studies have primarily mapped the visual and semantic selectivity of neur…
Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models
Andrew F. Luo, Margaret M. Henderson, Leila Wehbe +1
A long standing goal in neuroscience has been to elucidate the functional organization of the brain. Within higher visual cortex, functional accounts have remained relatively coars…