Related references
Note: Only part of the references are listed.Unsupervised Learning of Auroral Optical Flow for Recognition of Poleward Moving Auroral Forms
Qiuju Yang et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)
Temporal Segment Networks for Action Recognition in Videos
Limin Wang et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2019)
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes
Yiran Zhong et al.
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)
Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
Kensho Hara et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2017) (2017)
The something something video database for learning and evaluating visual common sense
Raghav Goyal et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Joao Carreira et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Learning Spatiotemporal Features with 3D Convolutional Networks
Du Tran et al.
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2015)
Poleward moving auroral forms (PMAFs) observed at the Yellow River Station: A statistical study of its dependence on the solar wind conditions
Z. Y. Xing et al.
JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS (2012)
Automatic recognition of poleward moving auroras from all-sky image sequences based on HMM and SVM
Qiuju Yang et al.
PLANETARY AND SPACE SCIENCE (2012)
Spatial texture based automatic classification of dayside aurora in all-sky images
Qian Wang et al.
JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS (2010)
Share Paper
