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Deep neural networks in social media content analysis - Adam Bielski

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How can we use the constantly growing number of photos and videos posted on social media? In this talk I will present three practical examples of deep neural networks applications to multimedia information extraction: logo detection, text extraction and popularity prediction.

Abstract
Every day large numbers of photos and videos are posted in social media. With the advent of modern deep learning, it is now possible to automatically analyze this content to get more in-depth insights.

In this talk I will present three hands-on examples of how deep neural networks can be applied for social media content analysis. First, I'll present our neural network architecture used to detect logotypes in the videos given a limited amount of training data. Then I will show a working example of text-in-the-wild extraction (detection and recognition) pipeline. Last but not least, I'll show how video thumbnails can be used to predict video popularity.

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Category
PyData Warsaw 2017

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