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  1. Only showing results from researchr.org

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  2. Exploring ROI size in deep learning based lipreading. Alexandros Koumparoulis, Gerasimos Potamianos, Youssef Mroueh, Steven J. Rennie. Exploring ROI size in deep learning based lipreading. In Slim Ouni, Chris Davis 0001, Alexandra Jesse, Jonas Beskow, editors, Auditory-Visual Speech Processing, AVSP 2017, Stockholm, Sweden, 25-26 August 2017.
  3. A Review on Deep Learning-Based Automatic Lipreading. Carlos Santos, António Cunha, Paulo Jorge Simães Coelho. A Review on Deep Learning-Based Automatic Lipreading. In António Cunha, Nuno M. Garcia, Jorge Marx Gómez, Sandra Pereira, editors, Wireless Mobile Communication and Healthcare - 11th EAI International Conference, MobiHealth 2022, Virtual Event, November 30 - December 2, 2022 ...
  4. Set-Top Box Automated Lip-Reading Controller Based on Convolutional Neural Network. In Isabel L. Nunes , editor, Advances in Human Factors and Systems Interaction - Proceedings of the AHFE 2019 International Conference on Human Factors and Systems Interaction, Washington, DC, USA, July 24-28, 2019 .
  5. conf.researchr.org

    Deep Learning (DL) has been successfully applied to a wide range of application domains, including safety-critical ones. Several DL testing approaches have been recently proposed in the literature but none of them aims to assess how different interpretable features of the generated inputs affect the system's behaviour.
  6. Lipreading Model Based On Whole-Part Collaborative Learning. Weidong Tian, Housen Zhang, Chen Peng, Zhong-Qiu Zhao. Lipreading Model Based On Whole-Part Collaborative Learning. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23-27 May 2022. pages 2425-2429, IEEE, 2022.
  7. conf.researchr.org

    Deep Learning (DL) solutions are increasingly adopted, but how to test them remains a major open research problem. Existing and new testing techniques have been proposed for and adapted to DL systems, including mutation testing. However, no approach has investigated the possibility to simulate the effects of real DL faults by means of mutation ...
  8. MobiLipNet: Resource-Efficient Deep Learning Based Lipreading Alexandros Koumparoulis , Gerasimos Potamianos . In Gernot Kubin , Zdravko Kacic , editors, Interspeech 2019, 20th Annual Conference of the International Speech Communication Association, Graz, Austria, 15-19 September 2019 .
  9. Researchr. Researchr is a web site for finding, collecting, sharing, and reviewing scientific publications, for researchers by researchers. Sign up for an account to create a profile with publication list, tag and review your related work, and share bibliographies with your co-authors.
  10. Deep Reinforcement Learning-Based 3D Exploration with a Wall Climbing Robot. Arya Das, Raju Halder, Atul Thakur. Deep Reinforcement Learning-Based 3D Exploration with a Wall Climbing Robot. In IEEE Region 10 Conference, TENCON 2021, Auckland, New Zealand, December 7-10, 2021. pages 863-868, IEEE, 2021.

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