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Resources for Computer-Based Sign Recognition from Video, and the Criticality of Consistency of Gloss Labeling across Multiple Large ASL Video Corpora
The WLASL purports to be “the largest video dataset for Word-Level American Sign Language (ASL) recognition.” It brings together various publicly shared video collections that could be quite valuable for sign recognition research, and it has been used extensively for such research.
Bidirectional Skeleton-Based Isolated Sign Recognition using Graph Convolutional Networks
To improve computer-based recognition from video of isolated signs from American Sign Language (ASL), we propose a new skeleton-based method that involves explicit detection of the start and end frames of signs, trained on the ASLLVD dataset; it uses linguistically relevant parameters based on the skeleton input.
Isolated Sign Recognition using ASL Datasets with Consistent Text-based Gloss Labeling and Curriculum Learning
We present a new approach for isolated sign recognition, which combines a spatial-temporal Graph Convolution Network (GCN) architecture for modeling human skeleton keypoints with late fusion of both the forward and backward video streams, and we explore the use of curriculum learning.
OmniLabel: A Challenging Benchmark for Language-Based Object Detection
With more than 28K unique object descriptions on over 25K images, OmniLabel provides a challenging benchmark with diverse and complex object descriptions in a naturally open-vocabulary setting.
