7 papers
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…
Elastic Attention Cores for Scalable Vision Transformers
Alan Z. Song, Yinjie Chen, Mu Nan +8
Vision Transformers (ViTs) achieve strong data-driven scaling by leveraging all-to-all self-attention. However, this flexibility incurs a computational cost that scales quadratical…
Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
Mu Nan, Muquan Yu, Weijian Mai +12
Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computationa…
Towards Interpretable Visual Decoding with Attention to Brain Representations
Pinyuan Feng, Hossein Adeli, Wenxuan Guo +3
Recent work has demonstrated that complex visual stimuli can be decoded from human brain activity using deep generative models, offering new ways to probe how the brain represents…
Transformer brain encoders explain human high-level visual responses
Hossein Adeli, Sun Minni, Nikolaus Kriegeskorte
A major goal of neuroscience is to understand brain computations during visual processing in naturalistic settings. A dominant approach is to use image-computable deep neural netwo…
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…