More and more, AI is trying to make machines teach themselves with a minimum of human guidance. So-called self-supervision is an element that can be added to lots of machine learning tasks so that a computer learns with less human help, perhaps someday with none at all.
Scientists at China's Sun Yat-Sen University and Hong Kong Polytechnic University use self-supervision in a new bit of research to help a computer learn the pose of a human figure in a video clip.
Understanding what a person is doing in a picture is its own rich vein of machine learning research, useful for a whole number of things including video surveillance. But such methods rely on "annotated" data sets where labels are carefully applied to the orientation of the joints of the body.
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That's a problem because larger and larger "deep" neural networks are hungry for more and more data, but there isn't always enough labeled data to feed the network.
So, the Sun Yat-Sen researchers set out to show a neural network can refine its understanding by continually comparing the guesses of multiple networks with one another, ultimately lessening the need for the "ground truth" afforded by a labeled data set.
As the authors put it, the prior efforts for inferring a human pose have achieved success, but at the expense of a "time-consuming network architecture (e.g., ResNet-50) and limited scalability for all scenarios