Debanjan Datta
Gerald Friedland
ABSTRACT
As the field of multimedia computing has grown rapidly, so has the need for larger datasets[5] and increased modeling capacity. Navigating this complex landscape often necessitates the use of sophisticated tools and cloud architectures, which all need to be addressed before the actual research commences. Recently, AutoML, an innovation previously exclusive to tabular data, has expanded to encompass multimedia data. This development has the potential to greatly streamline the research process, allowing researchers to shift their focus from model construction to the core content of their problems. In doing so, AutoML not only optimizes resource utilization but also boosts the reproducibility of results. The aim of this tutorial is to acquaint the multimedia community with AutoML technologies, underscoring their advantages and their practical applications in the field.
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- Xin He, Kaiyong Zhao, and Xiaowen Chu. Automl: A survey of the state-of-the-art. Knowledge-Based Systems, 212:106622, 2021.Google ScholarCross Ref
- M. Minsky and S. Papert. Perceptrons: An Introduction to Computational Geometry. MIT Press, 1969.Google ScholarDigital Library
- Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. Yfcc100m: The new data in mul- timedia research. In Proceedings of the 2016 ACM on Conference on Multimedia Conference, pages 242--251. ACM, 2016.Google Scholar
Index Terms
- Efficient Multimedia Computing: Unleashing the Power of AutoML
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