SLRTP 2020: The Sign Language Recognition, Translation & Production Workshop

The objective of the “Sign Language Recognition, Translation & Production” (SLRTP 2020) Workshop was to bring together researchers who focus on the various aspects of sign language understanding using tools from computer vision and linguistics. The workshop sought to promote a greater linguistic and historical understanding of sign languages within the computer vision community, to foster new collaborations and to identify the most pressing challenges for the field going forwards. The workshop was held in conjunction with the European Conference on Computer Vision (ECCV), 2020.

[1]  Lale Akarun,et al.  Sign Language Recognition for Assisting the Deaf in Hospitals , 2016, HBU.

[2]  Florian Metze,et al.  How2: A Large-scale Dataset for Multimodal Language Understanding , 2018, NIPS 2018.

[3]  Karen Livescu,et al.  Fingerspelling Recognition in the Wild With Iterative Visual Attention , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[4]  Meredith Ringel Morris,et al.  Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective , 2019, ASSETS.

[5]  Jesse Read,et al.  Attention is All You Sign: Sign Language Translation with Transformers , 2020 .

[6]  Xavier Giró-i-Nieto,et al.  Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses , 2020, ArXiv.

[7]  Kenneth P. Camilleri,et al.  Phonologically-Meaningful Subunits for Deep Learning-Based Sign Language Recognition , 2020, ECCV Workshops.

[8]  L. Akarun,et al.  Neural Sign Language Translation by Learning Tokenization , 2020, 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020).

[9]  Lale Akarun,et al.  Score-level Multi Cue Fusion for Sign Language Recognition , 2020, ECCV Workshops.

[10]  Oscar Koller,et al.  Quantitative Survey of the State of the Art in Sign Language Recognition , 2020, ArXiv.

[11]  Annelies Braffort,et al.  Towards Continuous Recognition of Illustrative and Spatial Structures in Sign Language , 2020 .

[12]  Petros Maragos,et al.  Exploiting 3D Hand Pose Estimation in Deep Learning-Based Sign Language Recognition from RGB Videos , 2020, ECCV Workshops.

[13]  Ioannis Tsochantaridis,et al.  Real-Time Sign Language Detection using Human Pose Estimation , 2020, ECCV Workshops.

[14]  P. Maragos,et al.  3D Hands, Face and Body Extraction for Sign Language Recognition , 2020 .

[15]  Murat Saraclar,et al.  Improving Keyword Search Performance in Sign Language with Hand Shape Features , 2020, ECCV Workshops.

[16]  Hermann Ney,et al.  Weakly Supervised Learning with Multi-Stream CNN-LSTM-HMMs to Discover Sequential Parallelism in Sign Language Videos , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Lale Akarun,et al.  Unsupervised Key Hand Shape Discovery of Sign Language Videos with Correspondence Sparse Autoencoders , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  B. Woll,et al.  A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia among British Sign Language Users , 2020, ECCV Workshops.

[19]  Michèle Gouiffès,et al.  Automatic Segmentation of Sign Language into Subtitle-Units , 2020, ECCV Workshops.

[20]  José Mario De Martino,et al.  Recognition of Affective and Grammatical Facial Expressions: A Study for Brazilian Sign Language , 2020, ECCV Workshops.

[21]  Andrew D. Back,et al.  A Plan for Developing an Auslan Communication Technologies Pipeline , 2020, ECCV Workshops.

[22]  Murat Saraclar,et al.  Unsupervised Discovery of Sign Terms by K-Nearest Neighbours Approach , 2020, ECCV Workshops.

[23]  Matt Huenerfauth,et al.  Effect of Ranking and Precision of Results on Users’ Satisfaction with Search-by-Video Sign-Language Dictionaries , 2020 .

[24]  Ahmet Alp Kindiroglu,et al.  BosphorusSign22k Sign Language Recognition Dataset , 2020, SIGNLANG.

[25]  Xavier Giro-i-Nieto,et al.  How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).