Summary of Convmixformer- a Resource-efficient Convolution Mixer For Transformer-based Dynamic Hand Gesture Recognition, by Mallika Garg et al.
ConvMixFormer- A Resource-efficient Convolution Mixer for Transformer-based Dynamic Hand Gesture Recognition
by Mallika Garg, Debashis Ghosh, Pyari Mohan Pradhan
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel architecture, ConvMixFormer, for dynamic hand gesture recognition that leverages transformer-based models. By replacing the self-attention mechanism with a convolutional layer-based token mixer, the proposed model reduces computational complexity and parameters compared to traditional transformers. Additionally, an efficient gate mechanism is employed to control feature flow within different stages of the model. The ConvMixFormer is evaluated on NVidia Dynamic Hand Gesture and Briareo datasets, achieving state-of-the-art results for single and multimodal inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new approach to recognizing dynamic hand gestures using transformer-based models. The goal is to create a more efficient model that can capture local spatial features while reducing computational complexity. To achieve this, the authors replace self-attention with a convolutional layer-based token mixer and use an efficient gate mechanism. The proposed model is tested on two datasets and outperforms other methods in terms of state-of-the-art results. |
Keywords
» Artificial intelligence » Gesture recognition » Self attention » Token » Transformer
