7 citations · 12 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
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-…
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…
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…