Sign Language Recognition On Rwth Phoenix

Metrics

Word Error Rate (WER)

Results

Performance results of various models on this benchmark

		Paper Title
SubUNets	40.7	SubUNets: End-To-End Hand Shape and Continuous Sign Language Recognition
CTF-MM	37.8	Connectionist Temporal Fusion for Sign Language Translation
DTN	36.5	Dense Temporal Convolution Network for Sign Language Translation
SAN	29.7	Context Matters: Self-Attention for Sign Language Recognition
Stochastic CSLR	25.3	Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition
CrossModal	24.0	Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space
SLRGAN	23.4	007: Democratically Finding The Cause of Packet Drops
DNF	22.86	A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training
VAC	22.1	Visual Alignment Constraint for Continuous Sign Language Recognition
MSKA-SLR	22.1	Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation
WRNN + LET	20.89	Multimodal Locally Enhanced Transformer for Continuous Sign Language Recognition
STMC	20.7	Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition
SMKD	20.5	Self-Mutual Distillation Learning for Continuous Sign Language Recognition
C2SLR	20.4	C2SLR: Consistency-Enhanced Continuous Sign Language Recognition
RadialCTC	20.2	Deep Radial Embedding for Visual Sequence Learning
SignBERT+	20	SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding
CorrNet + VAC + SMKD	19.4	Continuous Sign Language Recognition with Correlation Network
TCNet	18.9	TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions
TwoStream-SLR	18.4	Two-Stream Network for Sign Language Recognition and Translation
SlowFastSign	18.3	SlowFast Network for Continuous Sign Language Recognition

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Sign Language Recognition On Rwth Phoenix

Metrics

Results