no code implementations • LREC 2022 • Konstantinos M. Dafnis, Evgenia Chroni, Carol Neidle, Dimitri Metaxas
To improve computer-based recognition from video of isolated signs from American Sign Language (ASL), we propose a new skeleton-based method that involves explicit detection of the start and end frames of signs, trained on the ASLLVD dataset; it uses linguistically relevant parameters based on the skeleton input.
no code implementations • SLTAT (LREC) 2022 • Konstantinos M. Dafnis, Evgenia Chroni, Carol Neidle, Dimitri Metaxas
We present a new approach for isolated sign recognition, which combines a spatial-temporal Graph Convolution Network (GCN) architecture for modeling human skeleton keypoints with late fusion of both the forward and backward video streams, and we explore the use of curriculum learning.
no code implementations • SignLang (LREC) 2022 • Carol Neidle, Augustine Opoku, Carey Ballard, Konstantinos M. Dafnis, Evgenia Chroni, Dimitri Metaxas
The WLASL purports to be “the largest video dataset for Word-Level American Sign Language (ASL) recognition.” It brings together various publicly shared video collections that could be quite valuable for sign recognition research, and it has been used extensively for such research.
no code implementations • SignLang (LREC) 2022 • Zhaoyang Xia, Yuxiao Chen, Qilong Zhangli, Matt Huenerfauth, Carol Neidle, Dimitri Metaxas
We modify a motion-based image animation model to generate high-resolution videos with the signer identity changed, but with the preservation of linguistically significant motions and facial expressions.
1 code implementation • 27 Nov 2023 • Zhaoyang Xia, Carol Neidle, Dimitris N. Metaxas
While signers have expressed interest, for a variety of applications, in sign language video anonymization that would effectively preserve linguistic content, attempts to develop such technology have had limited success, given the complexity of hand movements and facial expressions.
no code implementations • 1 Nov 2023 • Carol Neidle
There have been recent advances in computer-based recognition of isolated, citation-form signs from video.
no code implementations • 19 Jan 2022 • Carol Neidle, Augustine Opoku, Dimitris Metaxas
These data have been used for many types of research in linguistics and in computer-based sign language recognition from video; examples of such research are provided in the latter part of this article.
no code implementations • LREC 2016 • Polina Yanovich, Carol Neidle, Dimitris Metaxas
In American Sign Language (ASL) as well as other signed languages, different classes of signs (e. g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties.
no code implementations • LREC 2014 • Bo Liu, Jingjing Liu, Xiang Yu, Dimitris Metaxas, Carol Neidle
Essential grammatical information is conveyed in signed languages by clusters of events involving facial expressions and movements of the head and upper body.
no code implementations • LREC 2014 • Mark Dilsizian, Polina Yanovich, Shu Wang, Carol Neidle, Dimitris Metaxas
Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions and off-plane rotations), and/or achieve limited success.
no code implementations • LREC 2012 • Zoya Gavrilov, Stan Sclaroff, Carol Neidle, Sven Dickinson
A framework is proposed for the detection of reduplication in digital videos of American Sign Language (ASL).
no code implementations • LREC 2012 • Dimitris Metaxas, Bo Liu, Fei Yang, Peng Yang, Nicholas Michael, Carol Neidle
This paper addresses the problem of automatically recognizing linguistically significant nonmanual expressions in American Sign Language from video.