Spatio-temporal predictive network for videos with physical properties

Yuka Aoyagi, Noboru Murata, Hidetomo Sakaino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a spatio-temporal predictive network with attention weighting of multiple physical Deep Learning (DL) models for videos with various physical properties. Previous approaches have been models with multiple branches for difference properties in videos, but the outputs of branches have been simply summed even with properties that change in time and space. In addition, it is difficult to train previous models for sufficient representations of physical properties in videos. Therefore, we propose the design of the spatio-temporal prediction network and the training method for videos with multiple physical properties, motivated by the Mixtures of Experts framework. Multiple spatio-temporal DL branches/experts for multiple physical properties and pixel-wise and expert-wise attention mechanism for adaptively integrating outputs of experts, i.e., Spatial-Temporal Gating Networks (STGNs) are proposed. Experts are trained with a vast amount of synthetic image sequences by physical equations and noise models. Instead, the whole network including STGNs is allowed to be trained only with a limited number of real datasets. Experiments on various videos, i.e., traffic, pedestrian, Dynamic Texture videos, and radar images, show the superiority of our proposed approach compared with previous approaches.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
PublisherIEEE Computer Society
Pages2268-2278
Number of pages11
ISBN (Electronic)9781665448994
DOIs
Publication statusPublished - 2021 Jun
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, United States
Duration: 2021 Jun 192021 Jun 25

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Country/TerritoryUnited States
CityVirtual, Online
Period21/6/1921/6/25

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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