Open Access
MATEC Web Conf.
Volume 277, 2019
2018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)
Article Number 02006
Number of page(s) 6
Section Data and Signal Processing
Published online 02 April 2019
  1. Kafle, K. and C. Kanan. Answer-Type Prediction for Visual Question Answering. in Computer Vision and Pattern Recognition. (2016). [Google Scholar]
  2. Malinowski, M. and M. Fritz. A multi-world approach to question answering about real-world scenes based on uncertain input. in Advances in Neural Information Processing Systems. (2014). [Google Scholar]
  3. Johnson, J., et al., CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning. (2017). [Google Scholar]
  4. Zhou, B., et al., Simple Baseline for Visual Question Answering. Computer Science, (2015). [Google Scholar]
  5. Andreas, J., et al. Neural Module Networks. in IEEE Conference on Computer Vision and Pattern Recognition. (2016). [Google Scholar]
  6. Johnson, J., et al., Inferring and Executing Programs for Visual Reasoning. (2017). [Google Scholar]
  7. Santoro, A., et al., A simple neural network module for relational reasoning. (2017). [Google Scholar]
  8. Sak, H., A. Senior, and F. Beaufays, Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition. Computer Science, (2014): p. 338-342. [Google Scholar]
  9. Andreas, J., et al., Deep Compositional Question Answering with Neural Module Networks. Computer Science, (2015). 27: p. 55-56. [Google Scholar]
  10. He, K., et al., Mask R-CNN. IEEE Transactions on Pattern Analysis & Machine Intelligence, (2017). PP(99): p. 1-1. [Google Scholar]
  11. Liu, W., et al. SSD: Single Shot MultiBox Detector. in European Conference on Computer Vision. (2016). [Google Scholar]
  12. Sutskever, I., O. Vinyals, and Q.V. Le, Sequence to Sequence Learning with Neural Networks. (2014). 4: p. 3104-3112. [Google Scholar]
  13. Srivastava, N., et al., Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, (2014). 15(1): p. 1929-1958. [Google Scholar]
  14. Yao, Y., L. Rosasco, and A. Caponnetto, On Early Stopping in Gradient Descent Learning. Constructive Approximation, (2007). 26(2): p. 289-315. [CrossRef] [Google Scholar]
  15. Gatys, L.A., A.S. Ecker, and M. Bethge, A Neural Algorithm of Artistic Style. Computer Science, (2015). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.