Survey on the attention based RNN model and its applications in computer vision

Abstract

The recurrent neural networks (RNN) can be used to solve the sequence to sequence problem, where both the input and the output have sequential structures. Usually there are some implicit relations between the structures. However, it is hard for the common RNN model to fully explore the relations between the sequences. In this survey, we introduce some attention based RNN models which can focus on different parts of the input for each output item, in order to explore and take advantage of the implicit relations between the input and the output items. The different attention mechanisms are described in detail. We then introduce some applications in computer vision which apply the attention based RNN models. The superiority of the attention based RNN model is shown by the experimental results. At last some future research directions are given.

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Cited by in corpus (8)

• Attention Mechanisms in Computer Vision: A Survey
• A General Survey on Attention Mechanisms in Deep Learning
• Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild
• Recurrent Attention Unit
• Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis
• Learning Sentence Representation with Guidance of Human Attention
• Attention Models for Point Clouds in Deep Learning: A Survey
• Attention-based Transfer Learning for Brain-computer Interface