Deep Architectures and Ensembles for Semantic Video Classification
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This work addresses the problem of accurate semantic labelling of short videos. We advance the state of the art by proposing a new residual architecture, with state-of-the art classification performance at significantly reduced complexity. Further, we propose four new approaches to diversity-driven multi-net ensembling, one based on fast correlation measure and three incorporating a DNN-based combiner. We show that significant performance gains can be achieved by "clever" ensembling of diverse nets and we investigate factors contributing to high diversity. Based on the extensive YouTube8M dataset, we perform a detailed evaluation of a broad range of deep architectures, including designs based on recurrent networks (RNN), feature space aggregation (FV, VLAD, BoW), simple statistical aggregation, mid-stage AV fusion and others, presenting for the first time an in-depth evaluation and analysis of their behaviour.
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