Image Captioning with SEBL Net: Squeeze and Excitation Block combined with Bi-Long-short term memory Network

Abstract

Rapid developments in the advancing Deep Learning (DL) made significant progress in the methodologies for Automated Captioning. Automatic captioning for digital images or videos is a great challenge in Artificial intelligence. Though most algorithms used Convolution Neural networks (CNN), this work emphasize the use of Squeeze and Excitation (SE) technique with the Long Short- Term Memory (LSTM). This combination works well to generate the caption from a sequence of words based on the learning. This proposed work bridges the gap between visual and language system by combining the two vital methodologies for image caption.

Keywords: Deep Learning, Image Captioning, Long-Short Term Memory Block, Squeeze and Excitation, Convolution Process