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Learning Event Representations by Encoding the Temporal Context

  • Catarina Dias
  • Mariella DimiccoliEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11131)

Abstract

This work aims at learning image representations suitable for event segmentation, a largely unexplored problem in the computer vision literature. The proposed approach is a self-supervised neural network that captures patterns of temporal overlap by learning to predict the feature vector of neighbor frames, given the one of the current frame. The model is inspired to recent experimental findings in neuroscience, showing that stimuli associated with similar temporal contexts are grouped together in the representational space. Experiments performed on image sequences captured at regular intervals have shown that a representation able to encode the temporal context provides very promising results on the task of temporal segmentation.

Keywords

Representation learning Event learning LSTM Neural networks 

Notes

Acknowledgments

This work was partially founded by TIN2015-66951-C2, SGR 1742, ICREA Academia 2014, Marató TV3 (20141510), Nestore Horizon2020 SC1-PM-15-2017 (769643) and CERCA. The funders had no role in the study design, data collection, analysis, and preparation of the manuscript. The authors gratefully acknowledge NVIDIA Corporation for the donation of the GPU used in this work.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversity of PortoPortoPortugal
  2. 2.Department of Mathematics and Computer ScienceUniversity of BarcelonaBarcelonaSpain
  3. 3.Computer Vision Center, Campus UABCerdanyola del Valles, BarcelonaSpain

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