  This paper sustains the position that the time has come for thinking of
learning machines that conquer visual skills in a truly human-like context,
where a few human-like object supervisions are given by vocal interactions and
pointing aids only. This likely requires new foundations on computational
processes of vision with the final purpose of involving machines in tasks of
visual description by living in their own visual environment under simple
man-machine linguistic interactions. The challenge consists of developing
machines that learn to see without needing to handle visual databases. This
might open the doors to a truly orthogonal competitive track concerning deep
learning technologies for vision which does not rely on the accumulation of
huge visual databases.
