Parameter-Free Average Attention Improves Convolutional Neural Network Performance (Almost) Free of Charge

• URL: http://arxiv.org/abs/2210.07828v1
• Date: Fri, 14 Oct 2022 13:56:43 GMT
• Title: Parameter-Free Average Attention Improves Convolutional Neural Network Performance (Almost) Free of Charge
• Authors: Nils K\"orber (Center for Artificial Intelligence in Public Health Research, Robert Koch Institute, Berlin, Germany)
• Abstract summary: We introduce a parameter-free attention mechanism called PfAAM, that is a simple yet effective module. PfAAM can be plugged into various convolutional neural network architectures with a little computational overhead and without affecting model size. This demonstrates its wide applicability as a general easy-to-use module for computer vision tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual perception is driven by the focus on relevant aspects in the surrounding world. To transfer this observation to the digital information processing of computers, attention mechanisms have been introduced to highlight salient image regions. Here, we introduce a parameter-free attention mechanism called PfAAM, that is a simple yet effective module. It can be plugged into various convolutional neural network architectures with a little computational overhead and without affecting model size. PfAAM was tested on multiple architectures for classification and segmentic segmentation leading to improved model performance for all tested cases. This demonstrates its wide applicability as a general easy-to-use module for computer vision tasks. The implementation of PfAAM can be found on https://github.com/nkoerb/pfaam.

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