New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.
“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.
The software tool NeuroTrALE is designed to quickly and efficiently process large amounts of brain imaging data semi-automatically.
This technique could lead to safer autonomous vehicles, more efficient AR/VR headsets, or faster warehouse robots.
DenseAV, developed at MIT, learns to parse and understand the meaning of language just by watching videos of people talking, with potential applications in multimedia search, language learning, and robotics.
The technique characterizes a material’s electronic properties 85 times faster than conventional methods.
“Alchemist” system adjusts the material attributes of specific objects within images to potentially modify video game models to fit different environments, fine-tune VFX, and diversify robotic training.
For the first time, researchers use a combination of MEG and fMRI to map the spatio-temporal human brain dynamics of a visual image being recognized.
By providing plausible label maps for one medical image, the Tyche machine-learning model could help clinicians and researchers capture crucial information.
FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.
