Visual vs internal attention mechanisms in deep neural networks for image classification and object detection. (March 2022)

• Record Type: Journal Article
• Authors: Obeso, Abraham Montoya; Benois-Pineau, Jenny; García Vázquez, Mireya Saraí; Acosta, Alejandro Álvaro Ramírez
• Is Part Of: Pattern recognition. Volume 123(2022)
• Publication Date: 2022-03
• Subjects: Deep learning -- Image classification -- Object detection -- Visual attention -- Saliency maps; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108411
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Visual attention integration into convolutional neural networks. Attention mechanisms in classification and object detection tasks. Visual attention as subjective saliency maps allows faster convergence and better performance. Abstract: The so-called "attention mechanisms" in Deep Neural Networks (DNNs) denote an automatic adaptation of DNNs to capture representative features given a specific classification task and related data. Such attention mechanisms perform both globally by reinforcing feature channels and locally by stressing features in each feature map. Channel and feature importance are learnt in the global end-to-end DNNs training process. In this paper, we present a study and propose a method with a different approach, adding supplementary visual data next to training images. We use human visual attention maps obtained independently with psycho-visual experiments, both in task-driven or in free viewing conditions, or powerful models for prediction of visual attention maps. We add visual attention maps as new data alongside images, thus introducing human visual attention into the DNNs training and compare it with both global and local automatic attention mechanisms. Experimental results show that known attention mechanisms in DNNs work pretty much as human visual attention, but still the proposed approach allows a faster convergence and better performance in image classification tasks.