Slashdot Mirror
Facebook Thinks Occlusion Is the Next Great Frontier For Image Recognition
An anonymous reader writes: Researchers at Facebook AI Research (FAIR) have published a paper contending that image recognition research is now advanced enough to consider the problem of occlusion, wherein the objects AI must identify are either partially cropped or partially hidden. Their solution is the predictably labor-expensive route of human annotation of existing image-set databases, in this case 'finishing off' occluded objects with vector outlines and assigning them a z-order. This article looks at the practical and even philosophical problems of getting IR algorithms to 'guess' objects usefully, and asks whether practical IR research might not be currently limited both by the use of over-specific image datasets and — in the field of neural networks — by problems of theory and limited 'local' processing power in critical real-time situations.
Facebook does conduct research into AI. They need such technology to more effectively mine their vast database for advertising information.
Occlusion handling is the difference between 'subject identified as Joe Bloggs' and 'Subject identified as Joe Blogs wearing Adidas trainers and posing in front of a Skoda. Increase targeting of well-known fashion brands, decrease targeting for automotive products.'
It will have to be. The ability to figure out what you're looking at with incomplete information is exactly what leads to optical illusions, you can't really have one without the other.
A bullet may have your name on it but splash damage is addressed "To whom it may concern."
Pull back. Wait a minute. Go right. Stop.
Enhance 57 to 19. Track 45 left. Stop.
Enhance 15 to 23.
Gimme a hard copy right there.
What about a kind of genetic algorithm to evolve candidate 3D models, and the model that best matches observations and context "wins". However, that is computationally intensive. But, it is highly parallelizable.
Table-ized A.I.