Open Access
Issue
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
Article Number 03013
Number of page(s) 8
Section Computing
DOI https://doi.org/10.1051/itmconf/20214003013
Published online 09 August 2021
  1. L. Gonog and Y. Zhou, “A Review: Generative Adversarial Networks,” 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 505–510, (2019) [Google Scholar]
  2. P. Hurtik and P. Hodakova, “FTIP: A tool for an image plagiarism detection,” 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), Fukuoka, Japan, pp. 42–47, (2015) [Google Scholar]
  3. Y. Lang, Y. He, F. Yang, J. Dong, H. Xue, “Which is Plagiarism: Fashion Image Retrieval based on Regional Representation for Design Protection,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2595–2604, (2020) [Google Scholar]
  4. Y. J. Cao, L. L. Jia, Y. X. Chen, N. Lin, C. Yang, B. Zhang, Z. Liu, X. X. Li, H. H. Dai, “Recent Advances of Generative Adversarial Networks in Computer Vision,” in IEEE Access, vol. 7, pp. 14985–15006, (2019) [Google Scholar]
  5. T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, X. He, “AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1316–1324, (2018) [Google Scholar]
  6. I. J. Goodfellow, J. P. Abadie, M. Mirza, B. Xu, D. W. Farley, S. Ozair, A. Courville, Y. Bengio, “Generative Adversarial Nets,” arXiv preprint arXiv:1406.2661, vol: 1, (2014) [Google Scholar]
  7. K. Ak, A. Kassim, J. Lim and J. Y. Tham, “Attribute Manipulation Generative Adversarial Networks for Fashion Images,” 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10540–10549, (2019) [Google Scholar]
  8. I. Amerini, L. Galteri, R. Caldelli and A. Del Bimbo, “Deepfake Video Detection through Optical Flow Based CNN,” 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 1205–1207, (2019) [Google Scholar]
  9. M. Chen, Y. Qin, L. Qi and Y. Sun, “Improving Fashion Landmark Detection by Dual Attention Feature Enhancement,” 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3101–3104, (2019) [Google Scholar]
  10. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol: 1, pp. 886–893, (2005) [Google Scholar]
  11. K. E. Ak, J. H. Lim, J. Y. Tham and A. A. Kassim, “Efficient Multi-attribute Similarity Learning Towards Attribute-Based Fashion Search,” 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1671–1679, (2018) [Google Scholar]
  12. H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang and D. Metaxas, “StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks,” 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 5908–5916, (2017) [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.