  In a wide range of semantic segmentation tasks, fully convolutional neural
networks (F-CNNs) have been successfully leveraged to achieve state-of-the-art
performance. Architectural innovations of F-CNNs have mainly been on improving
spatial encoding or network connectivity to aid gradient flow. In this article,
we aim towards an alternate direction of recalibrating the learned feature maps
adaptively; boosting meaningful features while suppressing weak ones. The
recalibration is achieved by simple computational blocks that can be easily
integrated in F-CNNs architectures. We draw our inspiration from the recently
proposed 'squeeze & excitation' (SE) modules for channel recalibration for
image classification. Towards this end, we introduce three variants of SE
modules for segmentation, (i) squeezing spatially and exciting channel-wise,
(ii) squeezing channel-wise and exciting spatially and (iii) joint spatial and
channel 'squeeze & excitation'. We effectively incorporate the proposed SE
blocks in three state-of-the-art F-CNNs and demonstrate a consistent
improvement of segmentation accuracy on three challenging benchmark datasets.
Importantly, SE blocks only lead to a minimal increase in model complexity of
about 1.5%, while the Dice score increases by 4-9% in the case of U-Net. Hence,
we believe that SE blocks can be an integral part of future F-CNN
architectures.
