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.
Local Coordinate Coding (LCC), introduced in (, 2009), is a high dimensional nonlinear learning method that explicitly takes advantage of the geometric structure of the data. Its successful use in the winning system of last year's Pascal image classification Challenge (Everingham, 2009) shows that the ability to integrate geometric information is critical for some real world machine learning applications. This paper further develops the idea of integrating geometry in machine learning by extending the original LCC method to include local tangent directions. These new correction terms lead to better approximation of high dimensional nonlinear functions when the underlying data manifold is locally relatively flat. The method significantly reduces the number of anchor points needed in LCC, which not only reduces computational cost, but also improves prediction performance. Experiments are included to demonstrate that this method is more effective than the original LCC method on some image classification tasks. The objective of this paper is to design an embedding method mapping local features describing image (e.g. SIFT) to a higher dimensional representation used for image retrieval problem.