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• W2903314716 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." @default.
• W2903314716 created "2018-12-11" @default.
• W2903314716 creator A5022551740 @default.
• W2903314716 creator A5032345007 @default.
• W2903314716 date "2018-12-03" @default.
• W2903314716 modified "2023-09-27" @default.
• W2903314716 title "MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language." @default.
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