by Angela Guess
Will Knight reports in the MIT Technology Review, “A few years ago, a breakthrough in machine learning suddenly enabled computers to recognize objects shown in photographs with unprecedented—almost spooky—accuracy. The question now is whether machines can make another leap, by learning to make sense of what’s actually going on in such images. A new image database, called Visual Genome, could push computers toward this goal, and help gauge the progress of computers attempting to better understand the real world. Teaching computers to parse visual scenes is fundamentally important for artificial intelligence. It might not only spawn more useful vision algorithms, but also help train computers how to communicate more effectively, because language is so intimately tied to representation of the physical world.”
Knight goes on, “Visual Genome was developed by Fei-Fei Li, a professor who specializes in computer vision and who directs the Stanford Artificial Intelligence Lab, together with several colleagues. ‘We are focusing very much on some of the hardest questions in computer vision, which is really bridging perception to cognition,’ Li says. ‘Not just taking pixel data in and trying to makes sense of its color, shading, those sorts of things, but really turn that into a fuller understanding of the 3-D as well as the semantic visual world.’ Li and colleagues previously created ImageNet, a database containing more than a million images tagged according to their contents. Each year, the ImageNet Large Scale Visual Recognition Challenge tests the ability of computers to automatically recognize the contents of images.”
photo credit: Visual Genome