# ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos

arXiv: https://arxiv.org/abs/2208.07929 · HTML: https://mlsanity.com/paper/2208.07929/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 87 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://mlsanity.com/paper/2106.10270/) (2021)
- [Temporal Segment Networks for Action Recognition in Videos](https://mlsanity.com/paper/1705.02953/) (2017)
- [Evidential Deep Learning for Open Set Action Recognition](https://mlsanity.com/paper/2107.10161/) (2021)
- [Long-term Temporal Convolutions for Action Recognition](https://mlsanity.com/paper/1604.04494/) (2016)
- [DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing](https://mlsanity.com/paper/1611.01942/) (2016)
- [Self-supervised Video Transformer](https://mlsanity.com/paper/2112.01514/) (2021)
- [VideoLSTM Convolves, Attends and Flows for Action Recognition](https://mlsanity.com/paper/1607.01794/) (2016)
- [Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition](https://mlsanity.com/paper/1712.05134/) (2017)
- [Learning from Temporal Gradient for Semi-supervised Action Recognition](https://mlsanity.com/paper/2111.13241/) (2021)
- [TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition](https://mlsanity.com/paper/1703.10667/) (2017)
- [Simplified Minimal Gated Unit Variations for Recurrent Neural Networks](https://mlsanity.com/paper/1701.03452/) (2017)
- [Attention-Driven Body Pose Encoding for Human Activity Recognition](https://mlsanity.com/paper/2009.14326/) (2020)

## Built on by (most cited first, 7 of 7)

- [Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12003/) (2024)
- [Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review](https://mlsanity.com/paper/2301.12222/) (2023)
- [Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey](https://mlsanity.com/paper/2312.06718/) (2023)
- [RNNs, CNNs and Transformers in Human Action Recognition: A Survey and a Hybrid Model](https://mlsanity.com/paper/2407.06162/) (2024)
- [DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition](https://mlsanity.com/paper/2507.12426/) (2025)
- [A Novel Deep Hybrid Framework with Ensemble-Based Feature Optimization for Robust Real-Time Human Activity Recognition](https://mlsanity.com/paper/2508.18695/) (2025)
- [Context Patch Fusion With Class Token Enhancement for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2601.14718/) (2026)

## Abstract (arXiv)

Human activity recognition is an emerging and important area in computer vision which seeks to determine the activity an individual or group of individuals are performing. The applications of this field ranges from generating highlight videos in sports, to intelligent surveillance and gesture recognition. Most activity recognition systems rely on a combination of convolutional neural networks (CNNs) to perform feature extraction from the data and recurrent neural networks (RNNs) to determine the time dependent nature of the data. This paper proposes and designs two transformer neural networks for human activity recognition: a recurrent transformer (ReT), a specialized neural network used to make predictions on sequences of data, as well as a vision transformer (ViT), a transformer optimized for extracting salient features from images, to improve speed and scalability of activity recognition. We have provided an extensive comparison of the proposed transformer neural networks with the contemporary CNN and RNN-based human activity recognition models in terms of speed and accuracy.