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20182023
most citedQuantitative Survey of the State of the Art in Sign Language Recognition

59 citations · 70 across the 5 of their papers we have counts for

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5 papers · 1 filter

cs.CV2020

Multi-channel Transformers for Multi-articulatory Sign Language Translation

Necati Cihan Camgoz, Oscar Koller, Simon Hadfield +1

Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore. In this pa…

cs.CV2020★ 59 cited

Quantitative Survey of the State of the Art in Sign Language Recognition

Oscar Koller

This work presents a meta study covering around 300 published sign language recognition papers with over 400 experimental results. It includes most papers between the start of the…

cs.CV2020

Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation

Necati Cihan Camgoz, Oscar Koller, Simon Hadfield +1

Prior work on Sign Language Translation has shown that having a mid-level sign gloss representation (effectively recognizing the individual signs) improves the translation performa…

cs.CV2019

Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective

Danielle Bragg, Oscar Koller, Mary Bellard +9

Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, nat…

cs.CV2018

MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language

Hamid Reza Vaezi Joze, Oscar Koller

Sign language recognition is a challenging and often underestimated problem comprising multi-modal articulators (handshape, orientation, movement, upper body and face) that integra…