Devon: Deformable Volume Network for Learning Optical Flow

Yao Lu, Jack Valmadre, Heng Wang, Juho Kannala, Mehrtash Harandi, Philip H. S. Torr

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

We propose a new neural network module, Deformable Cost Volume, for learning large displacement optical flow. The module does not distort the original images or their feature maps and therefore avoids the artifacts associated with warping. Based on this module, a new neural network model is proposed. The full version of this paper can be found online (https://arxiv.org/abs/1802.07351).
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 Workshops
Subtitle of host publicationMunich, Germany, September 8-14, 2018, Proceedings, Part VI
PublisherSpringer
Pages673-677
Number of pages5
ISBN (Electronic)978-3-030-11024-6
ISBN (Print)978-3-030-11023-9
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Conference publication
EventEuropean Conference on Computer Vision - Munich, Germany
Duration: 8 Sept 201814 Sept 2018
Conference number: 15

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume11134
NameImage Processing, Computer Vision, Pattern Recognition, and Graphics
Volume11134

Conference

ConferenceEuropean Conference on Computer Vision
Abbreviated titleECCV
Country/TerritoryGermany
CityMunich
Period08/09/201814/09/2018

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