A new neural network approach captures the characteristics of a physical system’s dynamic motion from video, regardless of rendering configuration or image differences.

MIT CSAIL scientists created an algorithm to solve one of the hardest tasks in computer vision: assigning a label to every pixel in the world, without human supervision.

Researchers find similarities between how some computer-vision systems process images and how humans see out of the corners of our eyes.

The new machine-learning system can generate a 3D scene from an image about 15,000 times faster than other methods.

It was announced that MIT EECS professors Phillip Isola and Antonio Torralba received career awards at the 2021 The Conference on Computer Vision and Pattern Recognition (CVPR).