GestFormer: Multiscale Wavelet Pooling Transformer Network for Dynamic Hand Gesture Recognition
This work addresses the problem of computational inefficiency in transformers for gesture recognition, offering a domain-specific solution that is incremental in nature.
The authors tackled dynamic hand gesture recognition by proposing GestFormer, a resource-efficient transformer architecture that uses pooling and wavelet transforms, achieving enhanced performance with fewer parameters compared to traditional transformers on NVidia Dynamic Hand Gesture and Briareo datasets.
Transformer model have achieved state-of-the-art results in many applications like NLP, classification, etc. But their exploration in gesture recognition task is still limited. So, we propose a novel GestFormer architecture for dynamic hand gesture recognition. The motivation behind this design is to propose a resource efficient transformer model, since transformers are computationally expensive and very complex. So, we propose to use a pooling based token mixer named PoolFormer, since it uses only pooling layer which is a non-parametric layer instead of quadratic attention. The proposed model also leverages the space-invariant features of the wavelet transform and also the multiscale features are selected using multi-scale pooling. Further, a gated mechanism helps to focus on fine details of the gesture with the contextual information. This enhances the performance of the proposed model compared to the traditional transformer with fewer parameters, when evaluated on dynamic hand gesture datasets, NVidia Dynamic Hand Gesture and Briareo datasets. To prove the efficacy of the proposed model, we have experimented on single as well multimodal inputs such as infrared, normals, depth, optical flow and color images. We have also compared the proposed GestFormer in terms of resource efficiency and number of operations. The source code is available at https://github.com/mallikagarg/GestFormer.