# UniCon-Former: Unified Convolution Transformer is All You Need for Hand Gesture Recognition

arXiv: https://arxiv.org/abs/2608.13217 · HTML: https://mlsanity.com/paper/2608.13217/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [UniFormer: Unifying Convolution and Self-attention for Visual Recognition](https://mlsanity.com/paper/2201.09450/) (2022)
- [AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation](https://mlsanity.com/paper/2304.12301/) (2023)
- [Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition](https://mlsanity.com/paper/2008.09412/) (2020)
- [Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks](https://mlsanity.com/paper/1901.10323/) (2019)
- [Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal Training](https://mlsanity.com/paper/1812.06145/) (2018)
- [A robust and efficient video representation for action recognition](https://mlsanity.com/paper/1504.05524/) (2015)
- [Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition](https://mlsanity.com/paper/1804.07187/) (2018)
- [Res3ATN -- Deep 3D Residual Attention Network for Hand Gesture Recognition in Videos](https://mlsanity.com/paper/2001.01083/) (2020)
- [ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation](https://mlsanity.com/paper/2309.05674/) (2023)
- [Multi-Task and Multi-Modal Learning for RGB Dynamic Gesture Recognition](https://mlsanity.com/paper/2110.15639/) (2021)
- [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)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) capture local features efficiently but struggle with global context due to their limited receptive field. On the other hand, transformers effectively capture global dependencies through self-attention but suffer from high redundancy and computational costs. Thus, to leverage the advantages of both CNNs and transformers, we propose a unified model (UniCon-Former) that aims to provide robust and efficient performance on dynamic hand gesture recognition. The unified approach helps the model to learn both local and global features. At the beginning of each transformer stage, the convolution projections help in decreasing the dimension of the input vectors of the transformer block. This creates a pyramidal structure at each transformer stage. These features enable the UniCon-Former to reduce resource usage than vanilla transformers, making it flexible for learning multi-scale and high-resolution features, which is required in hand gesture recognition. We have performed experiments with NVGesture and Briareo datasets and achieved state-of-the-art results with fewer parameters and MACs.