4 papers
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…
Uni-ISP: Toward Unifying the Learning of ISPs from Multiple Mobile Cameras
Lingen Li, Mingde Yao, Xingyu Meng +3
Modern end-to-end image signal processors (ISPs) can learn complex mappings from RAW/XYZ data to sRGB (and vice versa), opening new possibilities in image processing. However, the…
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…