Papers Sign Language Recognition
“Sign Language Recognition” 태그가 달린 논문 348편 · 필터 해제
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 TransferDeep Learning-Based Sign Language Recognition from Videos and Cross-Lingual Translation to Indian Vernaculars
Sign language is a primary mode of communication for the global deaf and hard-of-hearing community, yet automated tools that recognize sign gestures from video and translate them into natural language text remain limited…
Sign Language RecognitionMachine TranslationWhat's the Point? Spatial Grammar & Index Resolution for Sign Language Processing
Sign language models are predominantly trained with gloss-sequence or text supervision, thereby under-modeling non-lexical and productive constructions. One comparatively tractable instance is spatial indexing: pointing …
Sign Language RecognitionEntity LinkingSign Language Recognition and Translation for Low-Resource Languages: Challenges and Pathways Forward
Sign languages are natural, visual-gestural languages used by Deaf communities worldwide. Over 300 distinct sign languages remain severely low-resource due to limited documentation, sparse datasets, and insufficient comp…
Sign Language RecognitionTransfer LearningCAST: Channel-Aware Spatial Transfer Learning with Pseudo-Image Radar for Sign Language Recognition
We propose CAST, a dual-stream architecture that utilizes channel-aware spatial transfer learning for isolated sign language recognition addressing the challenges of magnitude-only 60~GHz radar Range-Time Maps (RTM). The…
Sign Language RecognitionTransfer LearningSignMAE: Segmentation-Driven Self-Supervised Learning for Sign Language Recognition
Subtle hand differences make sign language recognition challenging, yet many existing methods rely on encoders pretrained on generic action datasets that poorly capture such fine-grained cues. We propose a self-supervise…
Sign Language RecognitionSelf-Supervised LearningRepresentation LearningOmniEncoder: See, Hear, and Feel Continuous Motion Like Humans With One Encoder
Recent advances in omni-modal large language models have enabled remarkable progress in joint vision-audio understanding. However, prevailing architectures rely on modality-specific encoders with a \emph{video-coarse, au…
Sign Language RecognitionComputational EfficiencySpeaker IdentificationCanonSLR: Canonical-View Guided Multi-View Continuous Sign Language Recognition
Continuous Sign Language Recognition (CSLR) has achieved remarkable progress in recent years; however, most existing methods are developed under single-view settings and thus remain insufficiently robust to viewpoint var…
Sign Language RecognitionSign Language Recognition in the Age of LLMs
Recent Vision Language Models (VLMs) have demonstrated strong performance across a wide range of multimodal reasoning tasks. This raises the question of whether such general-purpose models can also address specialized vi…
Sign Language RecognitionMultimodal ReasoningEfficientSign: An Attention-Enhanced Lightweight Architecture for Indian Sign Language Recognition
How do you build a sign language recognizer that works on a phone? That question drove this work. We built EfficientSign, a lightweight model which takes EfficientNet-B0 and focuses on two attention modules (Squeeze-and-…
Sign Language RecognitionState Space Models are Effective Sign Language Learners: Exploiting Phonological Compositionality for Vocabulary-Scale Recognition
Sign language recognition suffers from catastrophic scaling failure: models achieving high accuracy on small vocabularies collapse at realistic sizes. Existing architectures treat signs as atomic visual patterns, learnin…
Sign Language RecognitionRepresentation LearningLA-Sign: Looped Transformers with Geometry-aware Alignment for Skeleton-based Sign Language Recognition
Skeleton-based isolated sign language recognition (ISLR) demands fine-grained understanding of articulated motion across multiple spatial scales, from subtle finger movements to global body dynamics. Existing approaches …
Sign Language RecognitionRepresentation LearningDevelopment of ML model for triboelectric nanogenerator based sign language detection system
Sign language recognition (SLR) is vital for bridging communication gaps between deaf and hearing communities. Vision-based approaches suffer from occlusion, computational costs, and physical constraints. This work prese…
Sign Language RecognitionGesture RecognitionData AugmentationSTARK: Spatio-Temporal Attention for Representation of Keypoints for Continuous Sign Language Recognition
Continuous Sign Language Recognition (CSLR) is a crucial task for understanding the languages of deaf communities. Contemporary keypoint-based approaches typically rely on spatio-temporal encoding, where spatial interact…
Sign Language RecognitionGeometry-Aware Metric Learning for Cross-Lingual Few-Shot Sign Language Recognition on Static Hand Keypoints
Sign language recognition (SLR) systems typically require large labeled corpora for each language, yet the majority of the world's 300+ sign languages lack sufficient annotated data. Cross-lingual few-shot transfer, pret…
Sign Language RecognitionCross-Lingual TransferMetric Learning