Abstract
Providing rich and accurate metadata for indexing media content is a crucial problem for all the companies offering streaming entertainment services. These metadata are typically used to improve the result of search engines and to feed recommendation algorithms in order to yield recommendation lists matching user interests. In particular, the problem of labeling multimedia content with informative tags (able to accurately describe the topics associated with such content) is a relevant issue. Indeed, the labeling procedure is time-consuming and susceptible to errors process as it is usually performed by domain experts in a fully manual fashion. Recently, the adoption of Machine Learning based techniques to tackle this problem has been investigated but the lack of clean and labeled training data leads to the yield of weak predictive models. To address all these issues, in this work we define a Deep Learning based framework for semi-automatic multi-label classification integrating model prediction explanation tools. In particular, Model Explanation techniques allow for supporting the operator to perform labeling of the contents. A preliminary experimentation conducted on a real dataset demonstrates the quality of the proposed solution.
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References
Arevalo, J., Solorio, T., Montes-y Gómez, M., González, F.A.: Gated multimodal units for information fusion. arXiv preprint arXiv:1702.01992 (2017)
Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9268–9277 (2019)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)
Fish, E., Weinbren, J., Gilbert, A.: Rethinking movie genre classification with fine-grained semantic clustering. arXiv preprint arXiv:2012.02639 (2020)
Guarascio, M., Manco, G., Ritacco, E.: Deep learning. Encyclopedia Bioinform. Comput. Biol. ABC of Bioinform. 1–3, 634–647 (2018)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning - Volume 37. ICML 2015, pp. 448–456 (2015)
Kar, S., Maharjan, S., Solorio, T.: Folksonomication: predicting tags for movies from plot synopses using emotion flow encoded neural network. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2879–2891 (2018)
Kaya, M., Bilge, H.S.: Deep metric learning: a survey. Symmetry 11(9) (2019). https://doi.org/10.3390/sym11091066
Le Cun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning. ICML 2010, pp. 807–814 (2010)
Ribeiro, M.T., Singh, S., Guestrin, C.: “why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000–6010 (2017)
Wehrmann, J., Barros, R.C.: Movie genre classification: a multi-label approach based on convolutions through time. Appl. Soft Comput. 61, 973–982 (2017)
Wu, C., et al.: Exploiting user reviews for automatic movie tagging. Multimedia Tools Appl. 79(17), 11399–11419 (2020)
Acknowledgements
This work was partially supported by PON I &C 2014-2020 FESR MISE, Catch 4.0.
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Minici, M., Pisani, F.S., Guarascio, M., De Francesco, E., Lambardi, P. (2022). Learning and Explanation of Extreme Multi-label Deep Classification Models for Media Content. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_14
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