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- W1970191301 abstract "We consider the task of assigning categories (e.g., howto/cooking, sports/basketball, pet/dogs) to YouTube videos from video and text signals. We show that two complementary views on the data -- from the video and text perspectives -- complement each other and refine predictions. The contributions of the paper are threefold: (1) we show that a text-based classifier trained on imperfect predictions of the weakly supervised video content-based classifier is not redundant; (2) we demonstrate that a simple model which combines the predictions made by the two classifiers outperforms each of them taken independently; (3) we analyse such sources of text information as video title, description, user tags and viewers' comments and show that each of them provides valuable clues to the topic of the video." @default.
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- W1970191301 date "2011-07-24" @default.
- W1970191301 modified "2023-10-03" @default.
- W1970191301 title "Improved video categorization from text metadata and user comments" @default.
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- W1970191301 doi "https://doi.org/10.1145/2009916.2010028" @default.
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