We introduce a convolutional network that colorizes grayscale videos, but is constrained to copy colors from a single reference frame. - Carl Vondrick, Research Scientist, Machine Perception.The Google colorizing videos, as the scientists describe is a convolutional neural network, a kind of neural network that is architecturally well-suited to object tracking and video stabilization. It helps to learn and to follow multiple objects through occlusions. The first step taken was to teach the algorithm to color the gray-scale movies. Researchers taking clips from kinetics datasets like videos from YouTube that covers a wide range of human action converts the first frame to black and white. Then the neural network was trained by them to predict the original colors in subsequent frames.
RelatedIt was challenging to train the neural network as the model had to color moving objects and regions and was effectively forced to learn in order to track those objects and regions.
This forces the model to learn an explicit mechanism that we can use for tracking. - VondrickIn the result, the model can keep tabs on any region specified in the first frame of the video and, if given points of reference, can even track human poses. It is greatly impressive to witness how it outperforms several state-of-the-art colorization techniques.
Source: Google AI Blog
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