Abstract:
Attention models in deep learning algorithms gained popularity in recent years. In this work, we propose an attention mechanism on the basis of visual saliency maps injec...Show MoreMetadata
Abstract:
Attention models in deep learning algorithms gained popularity in recent years. In this work, we propose an attention mechanism on the basis of visual saliency maps injected into the Deep Neural Network (DNN) to enhance regions in feature maps during forward-backward propagation in training, and only forward propagation in testing. The key idea is to spatially capture features associated to prominent regions in images and propagate them to deeper layers. During training, first, we take as backbone the well-known AlexNet architecture and then the ResNet architecture to solve the task of building identification of Mexican architecture. Our model equipped with the "external" visual saliency-based attention mechanism outperforms models armed with squeeze-and-excitation units and double-attention blocks.
Published in: 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)
Date of Conference: 06-09 November 2019
Date Added to IEEE Xplore: 19 December 2019
ISBN Information: