AI News, Google Unveils Neural Network with “Superhuman” Ability to Determine the Location of Almost Any Image

Google Unveils Neural Network with “Superhuman” Ability to Determine the Location of Almost Any Image

But the job becomes significantly harder when the image lacks specific location cues or is taken indoors or shows a pet or food or some other detail.

To help, they bring to bear all kinds of knowledge about the world such as the type and language of signs on display, the types of vegetation, architectural styles, the direction of traffic, and so on.

Their new machine significantly outperforms humans and can even use a clever trick to determine the location of indoor images and pictures of specific things such as pets, food, and so on that have no location cues.

Weyand and co begin by dividing the world into a grid consisting of over 26,000 squares of varying size that depend on the number of images taken in that location.

“In total, PlaNet won 28 of the 50 rounds with a median localization error of 1131.7 km, while the median human localization error was 2320.75 km,” say Weyand and co.

“[This] small-scale experiment shows that PlaNet reaches superhuman performance at the task of geolocating Street View scenes.” An interesting question is how PlaNet performs so well without being able to use the cues that humans rely on, such as vegetation, architectural style, and so on.

But Weyand and co say they know why: 'We think PlaNet has an advantage over humans because it has seen many more places than any human can ever visit and has learned subtle cues of different scenes that are even hard for a well-traveled human to distinguish.” They go further and use the machine to locate images that do not have location cues, such as those taken indoors or of specific items.

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