March 28, 2024, 4:42 a.m. | Jingyang Huo, Yikai Wang, Xuelin Qian, Yun Wang, Chong Li, Jianfeng Feng, Yanwei Fu

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.18211v1 Announce Type: cross
Abstract: Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models. These approaches, while producing high-quality images, capture only a limited aspect of the complex information in fMRI signals and offer little detailed control over image creation. In contrast, this paper proposes to directly modulate the generation process of diffusion models using fMRI signals. Our approach, NeuroPictor, divides the fMRI-to-image process into three steps: i) fMRI calibrated-encoding, to tackle multi-individual …

arxiv cs.cv cs.lg fmri image pretraining type via

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Data Engineer

@ Quantexa | Sydney, New South Wales, Australia

Staff Analytics Engineer

@ Warner Bros. Discovery | NY New York 230 Park Avenue South