A General Survey on Attention Mechanisms in Deep Learning

TL;DR

This survey comprehensively reviews attention mechanisms in deep learning, presenting a unified framework, taxonomy, evaluation measures, and discussing future research directions.

Contribution

It introduces a general framework and taxonomy for attention mechanisms, unifying diverse models and providing a basis for evaluation and future research.

Findings

Provides a comprehensive taxonomy of attention mechanisms

Reviews evaluation measures for attention models

Discusses future research directions in attention mechanisms

Abstract

Attention is an important mechanism that can be employed for a variety of deep learning models across many different domains and tasks. This survey provides an overview of the most important attention mechanisms proposed in the literature. The various attention mechanisms are explained by means of a framework consisting of a general attention model, uniform notation, and a comprehensive taxonomy of attention mechanisms. Furthermore, the various measures for evaluating attention models are reviewed, and methods to characterize the structure of attention models based on the proposed framework are discussed. Last, future work in the field of attention models is considered.