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

arXiv: https://arxiv.org/abs/1601.06823 · HTML: https://mlsanity.com/paper/1601.06823/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2016.

## Built on by (most cited first, 15 of 15)

- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (2022)
- [Global-Local Attention for Emotion Recognition](https://mlsanity.com/paper/2111.04129/) (2021)
- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [GI-NAS: Boosting Gradient Inversion Attacks Through Adaptive Neural Architecture Search](https://mlsanity.com/paper/2405.20725/) (2024)
- [Attention Models for Point Clouds in Deep Learning: A Survey](https://mlsanity.com/paper/2102.10788/) (2021)
- [An Attention-based Method for Action Unit Detection at the 3rd ABAW Competition](https://mlsanity.com/paper/2203.12428/) (2022)
- [Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis](https://mlsanity.com/paper/2203.12570/) (2022)
- [Sequence Prediction Under Missing Data : An RNN Approach Without Imputation](https://mlsanity.com/paper/2208.08933/) (2022)
- [Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild](https://mlsanity.com/paper/2107.09510/) (2021)
- [Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Image](https://mlsanity.com/paper/2210.13646/) (2022)
- [Seasonal Encoder-Decoder Architecture for Forecasting](https://mlsanity.com/paper/2207.04113/) (2022)
- [Learning to Perceive in Deep Model-Free Reinforcement Learning](https://mlsanity.com/paper/2301.03730/) (2023)
- [Sensitive Image Classification by Vision Transformers](https://mlsanity.com/paper/2412.16446/) (2024)
- [Building LLM Agent Systems the Deep Learning Way: From Modular Design to Architecture Search](https://mlsanity.com/paper/2610.04961/) (2026)

## Abstract (arXiv)

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.