2 code implementations • ICCV 2019 • Bowen Shi, Aurora Martinez Del Rio, Jonathan Keane, Diane Brentari, Greg Shakhnarovich, Karen Livescu
In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media.
1 code implementation • 25 May 2022 • Bowen Shi, Diane Brentari, Greg Shakhnarovich, Karen Livescu
Existing work on sign language translation - that is, translation from sign language videos into sentences in a written language - has focused mainly on (1) data collected in a controlled environment or (2) data in a specific domain, which limits the applicability to real-world settings.
1 code implementation • CVPR 2021 • Bowen Shi, Diane Brentari, Greg Shakhnarovich, Karen Livescu
We propose a benchmark and a suite of evaluation metrics, some of which reflect the effect of detection on the downstream fingerspelling recognition task.
no code implementations • 26 Sep 2016 • Taehwan Kim, Jonathan Keane, Weiran Wang, Hao Tang, Jason Riggle, Gregory Shakhnarovich, Diane Brentari, Karen Livescu
Recognizing fingerspelling is challenging for a number of reasons: It involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected.
no code implementations • 26 Oct 2018 • Bowen Shi, Aurora Martinez Del Rio, Jonathan Keane, Jonathan Michaux, Diane Brentari, Greg Shakhnarovich, Karen Livescu
As the first attempt at fingerspelling recognition in the wild, this work is intended to serve as a baseline for future work on sign language recognition in realistic conditions.
no code implementations • ACL 2022 • Bowen Shi, Diane Brentari, Greg Shakhnarovich, Karen Livescu
This is an important task since significant content in sign language is often conveyed via fingerspelling, and to our knowledge the task has not been studied before.
no code implementations • 2 Sep 2023 • Marcelo Sandoval-Castaneda, Yanhong Li, Diane Brentari, Karen Livescu, Gregory Shakhnarovich
This paper presents an in-depth analysis of various self-supervision methods for isolated sign language recognition (ISLR).