Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/23149
Title: | Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation | Authors: | Partaourides, Harris Voskou, Andreas Kosmopoulos, Dimitrios I. Chatzis, Sotirios P. N. Metaxas, Dimitris |
Major Field of Science: | Natural Sciences | Field Category: | Computer and Information Sciences | Keywords: | Deep learning;Gloss to Text;Sign Language Translation;Weight compression | Issue Date: | 2020 | Source: | 15th International Symposium on Visual Computing, 2020, 5-7 October, San Diego | Project: | DeepSignNet: Video processing for Sign Language Recognition using Deep Bayesian Recurrent Neural Networks | Conference: | International Symposium on Visual Computing (ISVC) | Abstract: | Memory-efficient continuous Sign Language Translation is a significant challenge for the development of assisted technologies with real-time applicability for the deaf. In this work, we introduce a paradigm of designing recurrent deep networks whereby the output of the recurrent layer is derived from appropriate arguments from nonparametric statistics. A novel variational Bayesian sequence-to-sequence network architecture is proposed that consists of a) a full Gaussian posterior distribution for data-driven memory compression and b) a nonparametric Indian Buffet Process prior for regularization applied on the Gated Recurrent Unit non-gate weights. We dub our approach Stick-Breaking Recurrent network and show that it can achieve a substantial weight compression without diminishing modeling performance. | URI: | https://hdl.handle.net/20.500.14279/23149 | ISBN: | 9783030645588 | DOI: | 10.1007/978-3-030-64559-5_19 | Rights: | © Springer | Type: | Conference Papers | Affiliation : | Cyprus University of Technology University of Patras Rutgers University |
Publication Type: | Peer Reviewed |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
CORE Recommender
SCOPUSTM
Citations
50
3
checked on Mar 14, 2024
Page view(s) 50
272
Last Week
0
0
Last month
2
2
checked on Dec 25, 2024
Google ScholarTM
Check
Altmetric
This item is licensed under a Creative Commons License