Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers

By Oscar Koller, Jens Forster, Hermann Ney. Computer Vision and Image Understanding, 2015

585 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Developmental and Educational Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/562737212e6e63c75689a28f72d132ee3183cff3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Developmental and Educational Psychology

In Human-Computer Interaction

Citations in the corpus

A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training (2019, methodology): “We follow the experimental protocol adopted in [11] to split the database into training and testing subsets.”
SubUNets: End-to-End Hand Shape and Continuous Sign Language Recognition (2017, unknown): “[26] 23.”
Improving Sign Language Translation with Monolingual Data by Sign Back-Translation (2021, unknown): “However, the frames in [23] are cropped from a speciﬁc TV program of the weather forecast and thus in a low resolution.”
Sign Language Transformers: Joint End-to-End Sign Language Recognition and Translation (2020, methodology): “Such methods can be grouped under the banner of CSLR [40, 8].”
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language (2018, methodology): “We also have implemented [35] as the state-of-theart on PHOENIX2014 data set [26, 33].”
Neural Sign Language Translation (2018, methodology): “Therefore, the numerous advances in SLR [15] and even the move to the challenging Continuous SLR (CSLR) [33, 36] problem, do not allow us to provide meaningful interpretations”
Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization (2017, methodology): “Many previous works in the area of sign analysis [8, 16, 22, 25] use handcrafted features for spatio-temporal representations.”
Video-based Sign Language Recognition without Temporal Segmentation (2018, methodology): “There are primarily two categories: the hand-crafted features (Sun et al. 2013; Koller, Forster, and Ney 2015) and Convolutional Neural Network (CNN) based features (Tang et al. 2015; Huang et al. 2015; Pu, Zhou, and Li 2016).”
Weakly Supervised Learning with Multi-Stream CNN-LSTM-HMMs to Discover Sequential Parallelism in Sign Language Videos (2020, methodology): “There, we use exactly the same corpus partitioning as in [51] to ensure comparability to previously published results.”