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.
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