인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Existing sign language translation research has improved performance by utilizing glosses in sign language datasets. However, due to the limitations of manually annotating glosses, gloss-free models have been proposed. Since these approaches do not teach glosses in a supervised manner, the model must infer glosses indirectly by utilizing spatially detailed information. However, previous studies have been limited by their inability to provide three-dimensional information of real-world space. To tackle this issue, we propose Depth-GASLT, a new architecture that estimates depth information from 2D sign language images and incorporates it to reflect three-dimensional position of the hands. Our method leverages depth information to enhance the translation process by accurately capturing the spatial nuances of hand movements in sign language. To demonstrate the effectiveness of our method, we conduct experiments on the PHEONIX-14 dataset and show that our method outperform the baseline model by 0.45 points based on the BLEU-4 score. In addition, the combining experiment showed that the element-wise combining method was effective. The results emphasize the significance of three-dimensional information in sign language translation, and are expected to contribute as a basis for effectively reflecting it.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-24-02-090334093
