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Personalizing Fast-Forward Videos Based on Visual and Textual Features from Social Network
2020
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
The growth of Social Networks has fueled the habit of people logging their day-to-day activities, and long First-Person Videos (FPVs) are one of the main tools in this new habit. Semantic-aware fast-forward methods are able to decrease the watch time and select meaningful moments, which is key to increase the chances of these videos being watched. However, these methods can not handle semantics in terms of personalization. In this paper, we present a new approach to automatically creating
doi:10.1109/wacv45572.2020.9093330
dblp:conf/wacv/RamosSANN20
fatcat:ti4ri3wvkna7pdwnm3dr3o6zgu