Places is a 10 million image database for scene recognition. It contains images from more than 400 scene categories. Places-CNNs are trained to recognize scene context in human-level accuracy.
Using a new software technology combining the strengths of MEG (magneto-encephalography) and fMRI (functional magnetic resonance imaging), we are able to characterize the spatiotemporal dynamics of perceived or imagined events at the level of the whole human brain.
Moments is a large-scale human-annotated dataset of ~ 1 million (and growing) labelled videos corresponding to real-world actions, motions and events unfolding within three seconds.
The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.
Our goal is to understand the nature of intelligent behavior in the physical world, through the study of human intelligence and the design and implementation of intelligent robots.
We combine methods from computer science, neuroscience and cognitive science to explain and model how perception and cognition are realized in human and machine.
Our researchers create state-of-the-art systems to better recognize objects, people, scenes, behaviors and more, with applications in health-care, gaming, tagging systems and more.
Sometimes it’s easy to forget how good we humans are at understanding our surroundings. Without much thinking, we can describe objects and how they interact with each other.
