HyperAI초신경
홈
뉴스
최신 연구 논문
튜토리얼
데이터셋
백과사전
SOTA
LLM 모델
GPU 랭킹
컨퍼런스
전체 검색
소개
한국어
HyperAI초신경
Toggle sidebar
전체 사이트 검색...
⌘
K
홈
SOTA
Sign Language Recognition
Sign Language Recognition On Rwth Phoenix
Sign Language Recognition On Rwth Phoenix
평가 지표
Word Error Rate (WER)
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Word Error Rate (WER)
Paper Title
Repository
SLRGAN
23.4
007: Democratically Finding The Cause of Packet Drops
WRNN + LET
20.89
Multimodal Locally Enhanced Transformer for Continuous Sign Language Recognition
-
SubUNets
40.7
SubUNets: End-To-End Hand Shape and Continuous Sign Language Recognition
SAN
29.7
Context Matters: Self-Attention for Sign Language Recognition
VAC
22.1
Visual Alignment Constraint for Continuous Sign Language Recognition
SignBERT+
20
SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding
-
DNF
22.86
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training
CTF-MM
37.8
Connectionist Temporal Fusion for Sign Language Translation
-
C2SLR
20.4
C2SLR: Consistency-Enhanced Continuous Sign Language Recognition
TwoStream-SLR
18.4
Two-Stream Network for Sign Language Recognition and Translation
MSKA-SLR
22.1
Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation
CorrNet + VAC + SMKD
19.4
Continuous Sign Language Recognition with Correlation Network
-
Stochastic CSLR
25.3
Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition
SlowFastSign
18.3
SlowFast Network for Continuous Sign Language Recognition
DTN
36.5
Dense Temporal Convolution Network for Sign Language Translation
-
CrossModal
24.0
Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space
-
SMKD
20.5
Self-Mutual Distillation Learning for Continuous Sign Language Recognition
STMC
20.7
Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition
-
TCNet
18.9
TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions
RadialCTC
20.2
Deep Radial Embedding for Visual Sequence Learning
-
0 of 20 row(s) selected.
Previous
Next