41 citations · 41 across the 4 of their papers we have counts for
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UniCon-Former: Unified Convolution Transformer is All You Need for Hand Gesture Recognition
Mallika Garg, Debashis Ghosh, Pyari Mohan Pradhan
Convolutional Neural Networks (CNNs) capture local features efficiently but struggle with global context due to their limited receptive field. On the other hand, transformers effec…
OccRobNet : Occlusion Robust Network for Accurate 3D Interacting Hand-Object Pose Estimation
Mallika Garg, Debashis Ghosh, Pyari Mohan Pradhan
Occlusion is one of the challenging issues when estimating 3D hand pose. This problem becomes more prominent when hand interacts with an object or two hands are involved. In the pa…
Multiscaled Multi-Head Attention-based Video Transformer Network for Hand Gesture Recognition
Mallika Garg, Debashis Ghosh, Pyari Mohan Pradhan
Dynamic gesture recognition is one of the challenging research areas due to variations in pose, size, and shape of the signer's hand. In this letter, Multiscaled Multi-Head Attenti…
ConvMixFormer- A Resource-efficient Convolution Mixer for Transformer-based Dynamic Hand Gesture Recognition
Mallika Garg, Debashis Ghosh, Pyari Mohan Pradhan
Transformer models have demonstrated remarkable success in many domains such as natural language processing (NLP) and computer vision. With the growing interest in transformer-base…
MVTN: A Multiscale Video Transformer Network for Hand Gesture Recognition
Mallika Garg, Debashis Ghosh, Pyari Mohan Pradhan
In this paper, we introduce a novel Multiscale Video Transformer Network (MVTN) for dynamic hand gesture recognition, since multiscale features can extract features with variable s…
GestFormer: Multiscale Wavelet Pooling Transformer Network for Dynamic Hand Gesture Recognition
Mallika Garg, Debashis Ghosh, Pyari Mohan Pradhan
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,…