Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks

Understand

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.