What do I Annotate Next? An Empirical Study of Active Learning for Action Localization

Despite tremendous progress achieved in temporal action localization, state-of-the-art methods still struggle to train accurate models when annotated data is scarce. In this paper, we introduce a novel active learning framework for temporal localization that aims to mitigate this data dependency issue... (read more)

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