Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment
Sign language serves as a vital means of communication for individuals with hearing impairments, yet recognition resources for the over 100 distinct sign languages are severely lacking. In response, we present our work on sign language recognition using transfer learning and the domain adaptation method TA3N, which utilizes the Temporal Relational Network (TRN) module for aligning multi-scale temporal relations. Our findings highlight the superior performance of Domain Adaptation to neural network-based transfer learning, particularly in improving recognition of American Sign Language (ASL). O
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- FuzzyOverlapping authors or contributors · 62%zhayujie/CowAgent →
“Shared author/contributor keys: chan”
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Cross-Sign Language Transfer Learning Using Domain Adaptatio” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Keren Artiaga →
“Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment”
- LinkedLinked via arxiv author · 85%Chenyang Li →
“Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment”
- LinkedLinked via arxiv author · 85%Ercan Engin Kuruoglu →
“Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment”
- LinkedLinked via arxiv author · 85%Wai Kin →
“Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment”
- LinkedLinked via arxiv author · 85%Changwen Chen →
“Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment”