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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 광학 흐름 개선을 위해 OFR-Net이라는 새롭고 효과적인 컨벌루션 신경망 모델을 제안합니다. OFR-Net은 이미지와 광학 흐름 필드 간의 공간 상관 관계를 활용합니다. 인코더와 디코더 내부 및 사이의 잔여 연결, 조밀한 연결 및 건너뛰기 연결을 갖춘 피라미드형 코덱 구조를 채택하여 로컬 및 전역적으로 다양한 규모의 기능을 포괄적으로 융합합니다. 또한 대규모 변위 미세 조정 오류를 제한하기 위해 뒤틀림 손실을 도입합니다. FlyingChairs 및 MPI Sintel 데이터 세트에 대한 일련의 실험은 OFR-Net이 다양한 방법으로 예측된 광학 흐름을 효과적으로 개선할 수 있음을 보여줍니다.
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
부
@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},}
부
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 -