Humans are able to recognize and remember visual experiences effortlessly in rich detail, a feat that current artificial systems still struggle to accomplish.
Our goal is to understand how the human brain achieve this impressive computational feat to develop “cognitive machines”— biologically inspired computational models that can recognize and interact with the world like humans.
To achieve this goal, we investigate human perception and memory processes using a combination of human neuroimaging (fMRI and MEG/EEG), behavior, computational modeling and machine learning.
Image, Video and Audio Understanding
Generative Adversarial Networks for Audio Vision
Pattern recognition using supervised, semi-supervised and self-supervised learning
Spatiotemporal neural dynamics of human vision and audition
Neural signature of image memorability in human brain in adults and children
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