ITM Web Conf.
Volume 40, 2021International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
|Number of page(s)||6|
|Published online||09 August 2021|
- P. Prajapati, A. Thakkar, and A. Ganatra, “A survey and current research challenges in multi-label classification methods,” International Journal of Soft Computing and Engineering (IJSCE), vol. 2, no. 1, pp. 248–252, 2012. [Google Scholar]
- M. Ivasic-Kos, M. Pobar, and I. Ipsic, “Automatic movie posters classification into genres,” in International Conference on ICT Innovations, pp. 319–328, Springer, 2014. [Google Scholar]
- W.-T. Chu and H.-J. Guo, “Movie genre classification based on poster images with deep neural networks,” in Proceedings of the Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes, pp. 39–45, 2017. [Google Scholar]
- J. Wehrmann and R. C. Barros, “Movie genre classification: A multi-label approach based on convolutions through time,” Applied Soft Computing, vol. 61, pp. 973–982, 2017. [CrossRef] [Google Scholar]
- S. Sung and R. Chokshi, “Classification of movie posters to movie genres,” [Google Scholar]
- G. Barney and K. Kaya, “Predicting genre from movie posters,” 2019. [Google Scholar]
- J. A. Wi, S. Jang, and Y. Kim, “Poster-based multiple movie genre classification using inter-channel features,” IEEE Access, vol. 8, pp. 66615–66624, 2020. [CrossRef] [Google Scholar]
- A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artificial Intelligence Review, vol. 53, no. 8, pp. 5455–5516, 2020. [Google Scholar]
- E. Ben-Baruch, T. Ridnik, N. Zamir, A. Noy, I. Friedman, M. Protter, and L. Zelnik-Manor, “Asymmetric loss for multi-label classification,” arXiv preprint arXiv:2009.14119, 2020. [Google Scholar]
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