We introduce modifications to state-of-the-art approaches to aggregating local video descriptors by using attention mechanisms and function approximations. Rather than using ensembles of existing architectures, we provide an insight on creating new architectures. We demonstrate our solutions in the “The 2nd YouTube-8M Video Understanding Challenge”, by using frame-level video and audio descriptors. We obtain testing accuracy similar to the state of the art, while meeting budget constraints, and touch upon strategies to improve the state of the art. Model implementations are available in https: github.com pomonam LearnablePoolingMethods.
Despite the success and prominence of VLAD, numerous design choices in @cite_12 are left unexplained. For instance, the cluster similarities, @math , are computed via a simple linear transformation, where each local video descriptor is compared via dot product with a key per cluster centre, followed by a softmax layer. It is a straightforward idea to consider multiple keys per cluster centre, or to use multiple temporally close local descriptors per cluster similarity prediction.
@cite_12
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Common video representations often deploy an average or maximum pooling of pre-extracted frame features over time. Such an approach provides a simple means to encode feature distributions, but is likely to be suboptimal. As an alternative, we here explore combinations of learnable pooling techniques such as Soft Bag-of-words, Fisher Vectors , NetVLAD, GRU and LSTM to aggregate video features over time. We also introduce a learnable non-linear network unit, named Context Gating, aiming at modeling in-terdependencies between features. We evaluate the method on the multi-modal Youtube-8M Large-Scale Video Understanding dataset using pre-extracted visual and audio features. We demonstrate improvements provided by the Context Gating as well as by the combination of learnable pooling methods. We finally show how this leads to the best performance, out of more than 600 teams, in the Kaggle Youtube-8M Large-Scale Video Understanding challenge.