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Building A Size Constrained Predictive Models for Video Classification

  • Miha SkalicEmail author
  • David Austin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)

Abstract

Herein we present the solution to the \(2^\mathrm{nd}\) YouTube-8M video understanding challenge which placed \(1^\mathrm{st}\). Competition participants were tasked with building a size constrained video labeling model with a model size of less than 1 GB. Our final solution consists of several submodels belonging to Fisher vectors, NetVlad, Deep Bag of Frames and Recurrent neural networks model families. To make the classifier efficient under size constraints we introduced model distillation, partial weights quantization and training with exponential moving average.

Keywords

Deep learning Multi-label classification Video processing 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University Pompeu FabraBarcelonaSpain
  2. 2.Intel CorporationChandlerUSA

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