Proceedings ArticleDOI
Re-Sign: Re-Aligned End-to-End Sequence Modelling with Deep Recurrent CNN-HMMs
Oscar Koller,Sepehr Zargaran,Hermann Ney +2 more
- pp 3416-3424
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TLDR
This work proposes an algorithm that treats the provided training labels as weak labels and refines the label-to-image alignment on-the-fly in a weakly supervised fashion, and embedded into an HMM the resulting deep model continuously improves its performance in several re-alignments.Abstract:
This work presents an iterative re-alignment approach applicable to visual sequence labelling tasks such as gesture recognition, activity recognition and continuous sign language recognition. Previous methods dealing with video data usually rely on given frame labels to train their classifiers. However, looking at recent data sets, these labels often tend to be noisy which is commonly overseen. We propose an algorithm that treats the provided training labels as weak labels and refines the label-to-image alignment on-the-fly in a weakly supervised fashion. Given a series of frames and sequence-level labels, a deep recurrent CNN-BLSTM network is trained end-to-end. Embedded into an HMM the resulting deep model corrects the frame labels and continuously improves its performance in several re-alignments. We evaluate on two challenging publicly available sign recognition benchmark data sets featuring over 1000 classes. We outperform the state-of-the-art by up to 10% absolute and 30% relative.read more
Citations
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Proceedings ArticleDOI
Museum Guidance in Sign Language: the SignGuide project
Dimitrios Kosmopoulos,Constantinos Constantinopoulos,Maria Trigka,Dimitrios Papazachariou,Klimis Antzakas,Venetta Lampropoulou,Antonis A. Argyros,Iason Oikonomidis,Anastasios Roussos,Nikolaos Partarakis,Georgios Papagiannakis,Konstandinos Grigoriadis,Angeliki Koukouvou,Angeliki Moneda +13 more
TL;DR: An overview of the SignGuide project, which aims to develop a prototype interactive museum guide system for deaf visitors using mobile devices that will be able to receive visitors’ questions in their native (sign language) with regard to the exhibits and to provide additional content also in sign language using an avatar or video, utilizing techniques from the field of computer vision and machine learning.
Journal ArticleDOI
(2+1)D-SLR: an efficient network for video sign language recognition
TL;DR: A (2+1)D-SLR network based on (2+)D convolution, which is different from other methods in that the proposed network can achieve higher accuracy with a faster speed, and can not only achieve competitive accuracy but be much faster than current well-known sign language recognition methods.
Proceedings Article
Cross-Lingual Keyword Search for Sign Language
Nazif Can Tamer,Murat Saraclar +1 more
TL;DR: This work trains a weakly supervised keyword search model for sign language and improves the retrieval performance with two context modeling strategies, and compares the gloss retrieval and cross language retrieval performance on RWTH-PHOENIX-Weather 2014T dataset.
Journal ArticleDOI
How important is motion in sign language translation
Proceedings ArticleDOI
Independent Sign Language Recognition with 3d Body, Hands, and Face Reconstruction
TL;DR: In this article, a parametric model that enables joint extraction of 3D body shape, face and hands information from a single image is employed for independent sign language recognition, which leads to higher accuracy than recognition from raw RGB images and their optical flow fed into the I3D-type network for 3D action recognition and from 2D Openpose skeletons fed into a Recurrent Neural Network.
References
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Proceedings Article
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Karen Simonyan,Andrew Zisserman +1 more
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.