There’s an important technology that is commonly used in autonomous driving, medical imaging, and even Zoom virtual backgrounds: semantic segmentation. That’s the process of labelling pixels in an image as belonging to one of N classes (N being any number of classes), where the classes can be things like cars, roads, people, or trees. In the case of medical images, classes correspond to different organs or anatomical structures.
NVIDIA Research is working on semantic segmentation because it is a broadly applicable technology. We also believe that the techniques discovered to improve semantic segmentation may also help to improve many other dense prediction tasks, such as optical flow prediction (predicting the motion of objects), image super-resolution, and so on.
We have developed a new method for semantic segmentation that achieves record-setting state-of-the-art results in two common benchmarks: and , as seen in the following tables. IOU is intersection over union, a metric that describes the accuracy of semantic prediction.
In Cityscapes, this method achieves 85.4 IOU on the test set, a sizable improvement over other entries, given how close each of those scores are to each other.