  In recent years, deep neural networks (DNNs) achieved state-of-the-art
performance on several computer vision tasks. However, the one typical drawback
of these DNNs is the requirement of massive labeled data. Even though few-shot
learning methods address this problem, they often use techniques such as
meta-learning and metric-learning on top of the existing methods. In this work,
we address this problem from a neuroscience perspective by proposing a
hypothesis named Ikshana, which is supported by several findings in
neuroscience. Our hypothesis approximates the refining process of conceptual
gist in the human brain while understanding a natural scene/image. While our
hypothesis holds no particular novelty in neuroscience, it provides a novel
perspective for designing DNNs for vision tasks. By following the Ikshana
hypothesis, we design a novel neural-inspired CNN architecture named
IkshanaNet. The empirical results demonstrate the effectiveness of our method
by outperforming several baselines on the entire and subsets of the Cityscapes
and the CamVid semantic segmentation benchmarks.
