Christoph Feichtenhofer
Christoph Feichtenhofer
Research Scientist, Facebook AI Research (FAIR)
Verified email at fb.com - Homepage
TitleCited byYear
Convolutional two-stream network fusion for video action recognition
C Feichtenhofer, A Pinz, AP Zisserman
Computer Vision and Pattern Recognition (CVPR), 2016, 2016
10202016
Spatiotemporal Residual Networks for Video Action Recognition
C Feichtenhofer, A Pinz, R Wildes
Advances in Neural Information Processing Systems, 3468-3476, 2016
3342016
Spatiotemporal Multiplier Networks for Video Action Recognition
C Feichtenhofer, A Pinz, RP Wildes
Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, 2017
1862017
Detect to track and track to detect
C Feichtenhofer, A Pinz, A Zisserman
Proceedings of the IEEE International Conference on Computer Vision, 3038-3046, 2017
1112017
A perceptual image sharpness metric based on local edge gradient analysis
C Feichtenhofer, H Fassold, P Schallauer
IEEE Signal Processing Letters 20 (4), 379-382, 2013
642013
SlowFast Networks for Video Recognition
C Feichtenhofer, H Fan, J Malik, K He
International Conference on Computer Vision (ICCV), 2019, 2019
522019
Bags of Spacetime Energies for Dynamic Scene Recognition
C Feichtenhofer, A Pinz, RP Wildes
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 0
48*
Spacetime forests with complementary features for dynamic scene recognition.
C Feichtenhofer, A Pinz, RP Wildes
BMVC, 6, 2013
332013
Temporal Residual Networks for Dynamic Scene Recognition
C Feichtenhofer, A Pinz, RP Wildes
Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, 0
32*
Long-term feature banks for detailed video understanding
CY Wu, C Feichtenhofer, H Fan, K He, P Krahenbuhl, R Girshick
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
242019
Dynamically encoded actions based on spacetime saliency
C Feichtenhofer, A Pinz, RP Wildes
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015
242015
3D human pose estimation in video with temporal convolutions and semi-supervised training
D Pavllo, C Feichtenhofer, D Grangier, M Auli
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
222019
Fusing RFID and computer vision for probabilistic tag localization
M Goller, C Feichtenhofer, A Pinz
2014 IEEE International Conference on RFID (IEEE RFID), 89-96, 2014
202014
What have we learned from deep representations for action recognition?
C Feichtenhofer, A Pinz, RP Wildes, A Zisserman
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
192018
Dynamic scene recognition with complementary spatiotemporal features
C Feichtenhofer, A Pinz, RP Wildes
IEEE transactions on pattern analysis and machine intelligence 38 (12), 2389 …, 2016
182016
Modeling Human Motion with Quaternion-based Neural Networks
D Pavllo, C Feichtenhofer, M Auli, D Grangier
arXiv preprint arXiv:1901.07677, 2019
92019
Camera-based vehicle velocity estimation from monocular video
M Kampelmühler, MG Müller, C Feichtenhofer
arXiv preprint arXiv:1802.07094, 2018
52018
Spatio-temporal good features to track
C Feichtenhofer, A Pinz
Proceedings of the IEEE International Conference on Computer Vision …, 2013
42013
Learning discriminative motion features through detection
G Bertasius, C Feichtenhofer, D Tran, J Shi, L Torresani
arXiv preprint arXiv:1812.04172, 2018
32018
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
G Bertasius, C Feichtenhofer, D Tran, J Shi, L Torresani
arXiv preprint arXiv:1906.04016, 2019
12019
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