Hand-Gesture Recognition
31 papers with code • 2 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Hand-Gesture Recognition
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Datasets
IPN Hand
VIVA
MLGESTURE DATASET
Human Palm and Gloves Dataset | Human Body Parts Dataset
Latest papers
Optimizing the Deployment of Tiny Transformers on Low-Power MCUs
Moreover, we show that our MHSA depth-first tiling scheme reduces the memory peak by up to 6. 19x, while the fused-weight attention can reduce the runtime by 1. 53x, and number of parameters by 25%.
CASTER: A Computer-Vision-Assisted Wireless Channel Simulator for Gesture Recognition
In the proposed CASTER simulator, however, the training dataset can be simulated via existing videos.
GRLib: An Open-Source Hand Gesture Detection and Recognition Python Library
Hand gesture recognition systems provide a natural way for humans to interact with computer systems.
OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation
Continuous mid-air hand gesture recognition based on captured hand pose streams is fundamental for human-computer interaction, particularly in AR / VR.
Data-Free Class-Incremental Hand Gesture Recognition
humansensinglab/dfcil-hgr
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ICCV 2023
Our sampling scheme outperforms SOTA methods significantly on two 3D skeleton gesture datasets, the publicly available SHREC 2017, and EgoGesture3D -- which we extract from a publicly available RGBD dataset.
Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning Models
Savings were defined as the number of additional observations required when a Deep Learning model is trained on new hand gestures instead of a Few Shot Learning model.
Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike Trains
MansoorehMontazerin/Vision-Transformer_EMG
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Additionally, the CT-HGR framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals.
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
mims-harvard/tfc-pretraining
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Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15. 4% (F1 score) on average in one-to-one settings (e. g., fine-tuning an EEG-pretrained model on EMG data) and by 8. 4% (precision) in challenging one-to-many settings (e. g., fine-tuning an EEG-pretrained model for either hand-gesture recognition or mechanical fault prediction), reflecting the breadth of scenarios that arise in real-world applications.
HaGRID - HAnd Gesture Recognition Image Dataset
hukenovs/hagrid
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This paper introduces an enormous dataset, HaGRID (HAnd Gesture Recognition Image Dataset), to build a hand gesture recognition (HGR) system concentrating on interaction with devices to manage them.
Snapture -- A Novel Neural Architecture for Combined Static and Dynamic Hand Gesture Recognition
Our architecture enables learning both static and dynamic gestures: by capturing a so-called "snapshot" of the gesture performance at its peak, we integrate the hand pose along with the dynamic movement.
