Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective
원문 초록
Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture. Despite the need for deep interdisciplinary knowledge, existing research occurs in separate disciplinary silos, and tackles separate portions of the sign language processing pipeline. This leads to three key questions: 1) What does an interdisciplinary view of the current landscape reveal? 2) What are the biggest challenges facing the field? and 3) What are the calls to action for people working in the field? To help answer these questions, we brought together a diverse group of experts for a two-day workshop. This paper presents the results of that interdisciplinary workshop, providing key background that is often overlooked by computer scientists, a review of the state-of-the-art, a set of pressing challenges, and a call to action for the research community.
함께 살펴볼 논문
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HandReader: Advanced Techniques for Efficient Fingerspelling Recognition
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
Hierarchical Sub-action Tree for Continuous Sign Language Recognition
CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation
SignBart -- New approach with the skeleton sequence for Isolated Sign language Recognition
Gender Bias in English-to-Greek Machine Translation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.