• When humans look at a picture, they automatically make a general judgement call about what’s going on in the scene – they intuit setting and context, essentially. This is a crucial skill that current computer vision and machine learning systems have a lot of trouble with, which can hinder the ability of intelligent systems designed to take on tasks like, say, driving or delivering packages.
  • Researchers weren’t entirely sure why their scene recognition tech was achieving about 50 percent accuracy when it came to labeling scenes, but then they noticed that the system was picking out specific visual features within the image and returning to those; the theory is, it was doing something akin to what we do: recognizing parts as emblematic of the whole.
  • The team is hoping that this means object recognition and scene recognition are not only coincident in high-level machine learning systems, but mutually reinforcing, meaning as one gets better as does the other.

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