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Multi-concept learning is an important problem in multimedia content analysis and retrieval. It connects two key components in the multimedia semantic ecosystem: Multimedia lexicon and semantic concept detection. This paper aims to answer two questions related to multi-concept learning: Does a large-scale lexicon help concept detection? How many concepts are enough? The study on a large-scale lexicon shows that more concepts indeed help improve detection performance. The gain is statistically significant with more than 40 concepts and saturates at over 200.
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