UniCon-Former: Unified Convolution Transformer is All You Need for Hand Gesture Recognition
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 ...
| Authors | Mallika Garg, Debashis Ghosh, Pyari Mohan Pradhan |
|---|---|
| Primary category | cs.CV |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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