Challenging Images For Minds and Machines

Amir Rosenfeld, John K. Tsotsos

Abstract:

There is no denying the tremendous leap in the performance of machine learning
methods in the past half-decade. Some might even say that specific sub-fields
in pattern recognition, such as machine-vision, are as good as solved, reaching
human and super-human levels. Arguably, lack of training data and computation
power are all that stand between us and solving the remaining ones. In this position
paper we underline cases in vision which are challenging to machines and even
to human observers. This is to show limitations of contemporary models that
are hard to ameliorate by following the current trend to increase training data,
network capacity or computational power. Moreover, we claim that attempting to
do so is in principle a suboptimal approach. We provide a taster of such examples
in hope to encourage and challenge the machine learning community to develop
new directions to solve the said difficulties.

TL;DR: A position paper showcasing failures of deep learning which in principle will not be solved by adding more data and contrasting supervision of humans and machines.

Keywords: New Challenges, Deep Learning Limitations