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Original Article

Deep Meme Automated Image Text Meme Production Via Convolutional Neural Networks

  • Paper ID 903
  • Volume 6, Issue 2
  • Pages 27-33
  • Published Online: March-April 2025
  • 8 Views
Open Access Peer Reviewed

Authors

  • Moni Chaurasiya1
  • Sanchi Deshmukh2
  • Kaikashan Siddavatam3
  • Sarita Kori4
  • Nikki Shukla5

1245University of Mumbai, Maharashtra, India. 3 Professor, University of Mumbai, Maharashtra, India.

Article Information

DOI
No DOI
Published Online
March-April 2025
Pages
27-33
Article Type
Original Article
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No DOI available for cross-reference

References

1. PavelBerkhin.2006. ASurveyofClusteringDataMiningTechniques. InGrouping Multidimensional Data.
2. Florian Colombo, Alexander Seeholzer, and Wulfram Gerstner. 2017. Deep artificial composer: A creative neural network model for
automated melody generation. In International Conference on Evolutionary and Biologically Inspired Music and Art. 81–96.
3. Shifman L. Memes in a digital world: Reconciling with a conceptual troublemaker. Journal of computer mediated communication.
2013;18(3):362-377. doi:10.1 111/jcc4.12013
4. Gal N, Shifman L, Kampf Z. “it gets better”: Internet memes and the construction of collective identity. New media & society.
2016;18(8):1698-1714,.
5. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, Show and tell: A neural image caption generator, 2015 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 2015.
6. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep con volutional neural networks.
Proceedings of the 25th International Conference on Neural Information Pro cessing Systems- Volume 1 (NIPS’12)
7. Xu, K. et al. Show, attend and tell: Neural image caption generation with visual attention. Proc. Interna tional Conference on Learning
Representations (2015).
8. Mun, J., Cho, M., Han, B.: Text-guided attention model for image captioning. AAAI (2016).
9. Ferrara E, JafariAsbagh M, Varol O, Qazvinian V, Menczer F, Flammini A. Clustering memes in social media. In: 2013 IEEE/ACM
International Conference on Advances in Social Networks Analysis and Mining (ASO.AM 2013). IEEE; 2013:548-555.
doi:10.1145/249 2517.2492530
10. Merriam-webster.com. (2018). Definition of MEME. [online] Available at: https://www.merriam webster.com/dictionary/meme
[Accessed 21 Mar. 2018].
11. GitHub. (2018). tensorflow/models. [online] Available at: https://github.com/tensorflow/models/tree /master/research/im2txt#model-
overview [Accessed 21 Mar. 2018].
12. Tensorflow authors, Official ‘Show and Tell: A neural image caption generator model’ implementation,
https://github.com/tensorflow/models
13. Peirson V and Tolunay2018. Abel L Peirson V and E Meltem Tolunay. 2018. Dank learning: Generating memes using deep neural
networks. arXiv preprint arXiv:1806.04510
14. Karpathy and Fei-Fei2015. Andrej Karpathy and Li Fei-Fei. 2015. Deep visual-semantic align ments for generating image
descriptions. In Proceedings of the IEEE conference on com puter vision and pattern recognition, pages 3128–3137
15. Wang and Wen2015. William Yang Wang and Miaomiao Wen. 2015. I can has cheezburger? a nonparanormal approach to combining
textual and visual information for predicting and gener ating popular meme descriptions. In Proceed ings of the 2015 Conference of
the North Amer ican Chapter of the Association for Computa tional Linguistics: Human Language Technolo gies, pages 355–365.
16. QuickMeme. 2016. Quick Meme Website. http://quickmeme.com/.
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