Visual vs internal attention mechanisms in deep neural networks for image classification and object detection

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 atten-tion 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 train-ing 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 inde-pendently 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 im-ages, 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.(c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates attention mechanisms in Deep Neural Networks (DNNs) and proposes a method that incorporates human visual attention. The proposed approach achieves faster convergence and better performance in image classification tasks compared to global and local automatic attention mechanisms.