Sign Language Recognition
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Benchmarks
RWTH-PHOENIX-Weather 2014
RWTH-PHOENIX-Weather 2014 T
CSL-Daily
AUTSL
WLASL-2000
WLASL100
LSA64
ChicagoFSWild
ChicagoFSWild+
Znaki
MSASL-1000
WLASL
BOBSL
Bukva
FDMSE-ISL
GSL
LIBRAS-UFOP
MINDS-Libras
Most implemented
Learning to Estimate 3D Hand Pose from Single RGB Images
BlazePose: On-device Real-time Body Pose tracking
Continuous Sign Language Recognition with Correlation Network
Skeleton Aware Multi-modal Sign Language Recognition
Papers
SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting
Continuous sign language recognition (CSLR) aims to recognize gloss sequences from unsegmented sign videos under weak sequence-level supervision. However, existing methods rely on sentence-level gloss annotations, provid…
Sign Language RecognitionRepresentation LearningCross-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 o…
Sign Language RecognitionTransfer LearningDomain AdaptationToward Deployable Bangla Sign Language Recognition with Expert-Validated Data and a Lightweight Attention-Based Model
Deaf and hard-of-hearing people in Bangladesh communicate mainly through Bangla Sign Language (BdSL). Automatic BdSL recognition on personal devices could widen access to education and services. Existing systems use cont…
Sign Language RecognitionA Low-Cost Hybrid Reservoir Computing Model for Isolated Sign Language Video Recognition
Sign language recognition (SLR) enhances communication between hearing and hearing-impaired individuals. Although deep learning (DL) has achieved promising performance in SLR, its high computational cost limits deploymen…
Sign Language RecognitionPhonological Perception of Sign Language Models
Sign languages are compositional systems where meaning arises by combining sublexical phonological parameters, such as handshape, location, and movement. While deep learning models for Sign Language Recognition (SLR) hav…
Sign Language RecognitionSIGNET: Motion-Level Knowledge Transfer for Cross-Language Sign Language Translation
Sign language translation (SLT) remains challenging due to its high spatio-temporal complexity, long sequences, and the need to model multiple articulators without relying on gloss annotations. Existing approaches are ty…
Sign Language TranslationSign Language RecognitionCross-Lingual Transfer