The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article propose un nouveau modèle de réseau neuronal convolutif efficace appelé OFR-Net pour le raffinement du flux optique. L'OFR-Net exploite la corrélation spatiale entre les images et les champs de flux optiques. Il adopte une structure de codec pyramidal avec des connexions résiduelles, des connexions denses et des connexions sautées au sein et entre l'encodeur et le décodeur, pour fusionner complètement les fonctionnalités de différentes échelles, localement et globalement. Nous introduisons également une perte de distorsion pour limiter les erreurs de raffinement de déplacement important. Une série d'expériences sur les ensembles de données FlyingChairs et MPI Sintel montrent que l'OFR-Net peut affiner efficacement le flux optique prédit par diverses méthodes.
Liping ZHANG
Tsinghua University
Zongqing LU
Tsinghua University
Qingmin LIAO
Tsinghua University
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Liping ZHANG, Zongqing LU, Qingmin LIAO, "OFR-Net: Optical Flow Refinement with a Pyramid Dense Residual Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 11, pp. 1312-1318, November 2020, doi: 10.1587/transfun.2020EAL2024.
Abstract: This paper proposes a new and effective convolutional neural network model termed OFR-Net for optical flow refinement. The OFR-Net exploits the spatial correlation between images and optical flow fields. It adopts a pyramidal codec structure with residual connections, dense connections and skip connections within and between the encoder and decoder, to comprehensively fuse features of different scales, locally and globally. We also introduce a warp loss to restrict large displacement refinement errors. A series of experiments on the FlyingChairs and MPI Sintel datasets show that the OFR-Net can effectively refine the optical flow predicted by various methods.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAL2024/_p
Copier
@ARTICLE{e103-a_11_1312,
author={Liping ZHANG, Zongqing LU, Qingmin LIAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={OFR-Net: Optical Flow Refinement with a Pyramid Dense Residual Network},
year={2020},
volume={E103-A},
number={11},
pages={1312-1318},
abstract={This paper proposes a new and effective convolutional neural network model termed OFR-Net for optical flow refinement. The OFR-Net exploits the spatial correlation between images and optical flow fields. It adopts a pyramidal codec structure with residual connections, dense connections and skip connections within and between the encoder and decoder, to comprehensively fuse features of different scales, locally and globally. We also introduce a warp loss to restrict large displacement refinement errors. A series of experiments on the FlyingChairs and MPI Sintel datasets show that the OFR-Net can effectively refine the optical flow predicted by various methods.},
keywords={},
doi={10.1587/transfun.2020EAL2024},
ISSN={1745-1337},
month={November},}
Copier
TY - JOUR
TI - OFR-Net: Optical Flow Refinement with a Pyramid Dense Residual Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1312
EP - 1318
AU - Liping ZHANG
AU - Zongqing LU
AU - Qingmin LIAO
PY - 2020
DO - 10.1587/transfun.2020EAL2024
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2020
AB - This paper proposes a new and effective convolutional neural network model termed OFR-Net for optical flow refinement. The OFR-Net exploits the spatial correlation between images and optical flow fields. It adopts a pyramidal codec structure with residual connections, dense connections and skip connections within and between the encoder and decoder, to comprehensively fuse features of different scales, locally and globally. We also introduce a warp loss to restrict large displacement refinement errors. A series of experiments on the FlyingChairs and MPI Sintel datasets show that the OFR-Net can effectively refine the optical flow predicted by various methods.
ER -