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Tiny, Blurry Pictures Find the Limits of Computer Image Recognition (arstechnica.com)
A new PNAS paper takes a look at just how different computer and human visual systems are. Humans can figure out that a mangled word "meant" something recognizable while a computer can't. Likewise with images: humans can piece together what a blurry image might depict based on small clues in the picture, where a computer would be at a loss. The authors of the PNAS paper used a set of blurry, tricky images to pinpoint the differences between computer vision models and the human brain.
They used pictures called "minimal recognizable configurations" (MIRCs) that were either so small or so low-resolution that any further reduction would prevent a person from being able to recognize them. The computer models did a better job after they were trained specifically on the MIRCs, but their accuracy was still low compared to human performance. The reason for this, the authors suggest, is that computers can't pick out the individual components of the image whereas humans can. This kind of interpretation is "beyond the capacities of current neural network models," the authors write.
They used pictures called "minimal recognizable configurations" (MIRCs) that were either so small or so low-resolution that any further reduction would prevent a person from being able to recognize them. The computer models did a better job after they were trained specifically on the MIRCs, but their accuracy was still low compared to human performance. The reason for this, the authors suggest, is that computers can't pick out the individual components of the image whereas humans can. This kind of interpretation is "beyond the capacities of current neural network models," the authors write.
This story is rather lacking without a single example of what they're talking about.
This seems to be the project the article is talking about: http://www.wisdom.weizmann.ac.il/~dannyh/Mircs/mircs.html
Ah, arrogance and stupidity, all in the same package. How efficient of you. -- Londo Mollari