Search Results for author: Diane Brentari

Found 6 papers, 2 papers with code

Open-Domain Sign Language Translation Learned from Online Video

no code implementations25 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.

Sign Language Translation Translation

Searching for fingerspelled content in American Sign Language

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.

Translation

Fingerspelling Detection in American Sign Language

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.

Pose Estimation

Fingerspelling recognition in the wild with iterative visual attention

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.

Hand Detection Sign Language Recognition

American Sign Language fingerspelling recognition in the wild

no code implementations26 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.

Frame Sign Language Recognition

Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation

no code implementations26 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.

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