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Sign Language Recognition
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Sign Language Recognition is a computer vision and natural language processing task that involves automatically recognizing and translating sign language gestures into written or spoken language. The goal of sign language recognition is to develop algorithms that can understand and interpret sign language, enabling people who use sign language as their primary mode of communication to communicate more easily with non-signers.
<span style="color:grey; opacity: 0.6">( Image credit: Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
HandReader: Advanced Techniques for Efficient Fingerspelling Recognition
Hierarchical Sub-action Tree for Continuous Sign Language Recognition
SignBart -- New approach with the skeleton sequence for Isolated Sign language Recognition
SLRNet: A Real-Time LSTM-Based Sign Language Recognition System
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks
Transfer Learning from Visual Speech Recognition to Mouthing Recognition in German Sign Language
Enhancing Mathematics Learning for Hard-of-Hearing Students Through Real-Time Palestinian Sign Language Recognition: A New Dataset
Logos as a Well-Tempered Pre-train for Sign Language Recognition
TSLFormer: A Lightweight Transformer Model for Turkish Sign Language Recognition Using Skeletal Landmarks
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition
SignX: The Foundation Model for Sign Recognition
Breaking the Barriers: Video Vision Transformers for Word-Level Sign Language Recognition
CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition
OLMD: Orientation-aware Long-term Motion Decoupling for Continuous Sign Language Recognition
BdSLW401: Transformer-Based Word-Level Bangla Sign Language Recognition Using Relative Quantization Encoding (RQE)
Representing Signs as Signs: One-Shot ISLR to Facilitate Functional Sign Language Technologies
Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition
Uni-Sign: Toward Unified Sign Language Understanding at Scale
Survey on Hand Gesture Recognition from Visual Input
Revolutionizing Communication with Deep Learning and XAI for Enhanced Arabic Sign Language Recognition
Comparison of Autoencoders for tokenization of ASL datasets
Beyond Words: AuralLLM and SignMST-C for Precise Sign Language Production and Bidirectional Accessibility
Training Strategies for Isolated Sign Language Recognition
Real-time Sign Language Recognition Using MobileNetV2 and Transfer Learning
SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction
AzSLD: Azerbaijani Sign Language Dataset for Fingerspelling, Word, and Sentence Translation with Baseline Software
Continuous Sign Language Recognition System using Deep Learning with MediaPipe Holistic
MM-WLAuslan: Multi-View Multi-Modal Word-Level Australian Sign Language Recognition Dataset
Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models
A Chinese Continuous Sign Language Dataset Based on Complex Environments
Deep Neural Network-Based Sign Language Recognition: A Comprehensive Approach Using Transfer Learning with Explainability
1DCNNTrans: BISINDO Sign Language Interpreters in Improving the Inclusiveness of Public Services
SCOPE: Sign Language Contextual Processing with Embedding from LLMs
Bengali Sign Language Recognition through Hand Pose Estimation using Multi-Branch Spatial-Temporal Attention Model
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