MULTIMEDIA DATA PROCESSING
Multimedia content has become pervasive. In this research area we mainly focus on the detection of hateful content, especially misogynistic, in viral contents such as memes. Moreover, we work towards the understanding of novel learning approaches by analyzing video lectures and provide a better insight on the user perceived interest in visual contents.
Meme analysis
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Multimedia content has become pervasive. In this research area we mainly focus on the detection of hateful content, especially misogynistic, in viral contents such as memes. Moreover, we work towards the understanding of novel learning approaches by analyzing video lectures and provide a better insight on the user perceived interest in visual contents.
Collect up-to-date online content.
Understand how misogyny is perceived by a heterogeneous population.
Define learning models considering a multimodal approach for multimedia content analysis, while exploiting natural language processing and image processing algorithms.
Video interestingness
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Video interestingness is often studied to boost user satisfaction. The goal is to create effective models that predict content engagement using deep learning, but these models are usually difficult for humans to interpret.
Particularly ...
We consider the use of handcrafted features to analyze signal behavior.
We use video saliency techniques to identify the most important frames in the videos.
We incorporate both audio and visual analysis, rather than focusing solely on visual data, to enhance our approach