REVIEW 6 cited by
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language
read the original abstract
Sign language recognition is a challenging and often underestimated problem comprising multi-modal articulators (handshape, orientation, movement, upper body and face) that integrate asynchronously on multiple streams. Learning powerful statistical models in such a scenario requires much data, particularly to apply recent advances of the field. However, labeled data is a scarce resource for sign language due to the enormous cost of transcribing these unwritten languages. We propose the first real-life large-scale sign language data set comprising over 25,000 annotated videos, which we thoroughly evaluate with state-of-the-art methods from sign and related action recognition. Unlike the current state-of-the-art, the data set allows to investigate the generalization to unseen individuals (signer-independent test) in a realistic setting with over 200 signers. Previous work mostly deals with limited vocabulary tasks, while here, we cover a large class count of 1000 signs in challenging and unconstrained real-life recording conditions. We further propose I3D, known from video classifications, as a powerful and suitable architecture for sign language recognition, outperforming the current state-of-the-art by a large margin. The data set is publicly available to the community.
Forward citations
Cited by 6 Pith papers
-
SIGMA-ASL: Sensor-Integrated Multimodal Dataset for Sign Language Recognition
SIGMA-ASL is a multimodal dataset with 93,545 word-level ASL clips from Kinect RGB-D, mmWave radar, and dual IMUs, plus benchmarking protocols for single- and multi-modal recognition.
-
SignVerse-2M: A Two-Million-Clip Pose-Native Universe of 55+ Sign Languages
SignVerse-2M provides a 2-million-clip multilingual pose-native dataset for sign language derived from public videos via DWPose preprocessing to enable robust modeling in real-world conditions.
-
CanonSLR: Canonical-View Guided Multi-View Continuous Sign Language Recognition
CanonSLR uses frontal-view teacher-student distillation and temporal motion enhancement to boost multi-view continuous sign language recognition, backed by new seven-view benchmarks PT14-MV and CSL-MV created from exi...
-
Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards
A survey indexes 120 sign-language datasets from 35 languages, identifies modality, annotation, and bias issues, and proposes a standardized 24-field datasheet with an open repository.
-
Sign Language Recognition in the Age of LLMs
Zero-shot VLM evaluation on WLASL300 reveals open-source models lag far behind supervised ISLR baselines, but proprietary models improve with scale and exhibit some visual-semantic alignment.
-
Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions
A literature review that categorizes deep learning approaches for visual hand gesture recognition, summarizes state-of-the-art methods across tasks, reviews datasets and metrics, and identifies challenges and future d...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.