From the MIT Technology Review:
Computers have always had trouble identifying objects in real images so it is not hard to believe that the winners of these competitions have always performed poorly compared to humans.
But all that changed in 2012 when a team from the University of Toronto in Canada entered an algorithm called SuperVision, which swept the floor with the opposition.
Today, Olga Russakovsky at Stanford University in California and a few pals review the history of this competition and say that in retrospect, SuperVision’s comprehensive victory was a turning point for machine vision. Since then, they say, machine vision has improved at such a rapid pace that today it rivals human accuracy for the first time.
So what happened in 2012 that changed the world of machine vision? The answer is a technique called deep convolutional neural networks which the Super Visison algorithm used to classify the 1.2 million high resolution images in the dataset into 1000 different classes.
This was the first time that a deep convolutional neural network had won the competition, and it was a clear victory. In 2010, the winning entry had an error rate of 28.2 percent, in 2011 the error rate had dropped to 25.8 percent. But SuperVision won with an error rate of only 16.4 percent in 2012 (the second best entry had an error rate of 26.2 percent). That clear victory ensured that this approach has been widely copied since then.
More here. [Thanks to Jennifer Oullette.]