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Sign Language Recognition On Rwth Phoenix

Metrics

Word Error Rate (WER)

Results

Performance results of various models on this benchmark

Paper TitleRepository
SubUNets40.7SubUNets: End-To-End Hand Shape and Continuous Sign Language Recognition-
CTF-MM37.8Connectionist Temporal Fusion for Sign Language Translation-
DTN36.5Dense Temporal Convolution Network for Sign Language Translation-
SAN29.7Context Matters: Self-Attention for Sign Language Recognition
Stochastic CSLR25.3Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition-
CrossModal24.0Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space-
SLRGAN23.4007: Democratically Finding The Cause of Packet Drops-
DNF22.86A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training-
VAC22.1Visual Alignment Constraint for Continuous Sign Language Recognition
MSKA-SLR22.1Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation
WRNN + LET20.89Multimodal Locally Enhanced Transformer for Continuous Sign Language Recognition-
STMC20.7Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition-
SMKD20.5Self-Mutual Distillation Learning for Continuous Sign Language Recognition-
C2SLR20.4C2SLR: Consistency-Enhanced Continuous Sign Language Recognition-
RadialCTC20.2Deep Radial Embedding for Visual Sequence Learning-
SignBERT+20SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding-
CorrNet + VAC + SMKD19.4Continuous Sign Language Recognition with Correlation Network
TCNet18.9TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions
TwoStream-SLR18.4Two-Stream Network for Sign Language Recognition and Translation
SlowFastSign18.3SlowFast Network for Continuous Sign Language Recognition
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Sign Language Recognition On Rwth Phoenix | SOTA | HyperAI