# Res3ATN -- Deep 3D Residual Attention Network for Hand Gesture Recognition in Videos

arXiv: https://arxiv.org/abs/2001.01083 · HTML: https://mlsanity.com/paper/2001.01083/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2020.

## Built on by (most cited first, 10 of 10)

- [GestFormer: Multiscale Wavelet Pooling Transformer Network for Dynamic Hand Gesture Recognition](https://mlsanity.com/paper/2405.11180/) (2024)
- [MVTN: A Multiscale Video Transformer Network for Hand Gesture Recognition](https://mlsanity.com/paper/2409.03890/) (2024)
- [ConvMixFormer- A Resource-efficient Convolution Mixer for Transformer-based Dynamic Hand Gesture Recognition](https://mlsanity.com/paper/2411.07118/) (2024)
- [Multiscaled Multi-Head Attention-based Video Transformer Network for Hand Gesture Recognition](https://mlsanity.com/paper/2501.00935/) (2025)
- [Data-Free Class-Incremental Gesture Recognition with Prototype-Guided Pseudo Feature Replay](https://mlsanity.com/paper/2505.20049/) (2025)
- [Weakly Supervised Attention Model for RV StrainClassification from volumetric CTPA Scans](https://mlsanity.com/paper/2107.12009/) (2021)
- [Border-SegGCN: Improving Semantic Segmentation by Refining the Border Outline using Graph Convolutional Network](https://mlsanity.com/paper/2109.05353/) (2021)
- [HeadPosr: End-to-end Trainable Head Pose Estimation using Transformer Encoders](https://mlsanity.com/paper/2202.03548/) (2022)
- [3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition](https://mlsanity.com/paper/2204.08460/) (2022)
- [UniCon-Former: Unified Convolution Transformer is All You Need for Hand Gesture Recognition](https://mlsanity.com/paper/2608.13217/) (2026)

## Abstract (arXiv)

Hand gesture recognition is a strenuous task to solve in videos. In this paper, we use a 3D residual attention network which is trained end to end for hand gesture recognition. Based on the stacked multiple attention blocks, we build a 3D network which generates different features at each attention block. Our 3D attention based residual network (Res3ATN) can be built and extended to very deep layers. Using this network, an extensive analysis is performed on other 3D networks based on three publicly available datasets. The Res3ATN network performance is compared to C3D, ResNet-10, and ResNext-101 networks. We also study and evaluate our baseline network with different number of attention blocks. The comparison shows that the 3D residual attention network with 3 attention blocks is robust in attention learning and is able to classify the gestures with better accuracy, thus outperforming existing networks.