Christoph Feichtenhofer
Christoph Feichtenhofer
Research Scientist, Facebook AI Research (FAIR)
Adresse e-mail validée de fb.com - Page d'accueil
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Convolutional two-stream network fusion for video action recognition
C Feichtenhofer, A Pinz, AP Zisserman
Computer Vision and Pattern Recognition (CVPR), 2016, 2016
20012016
Spatiotemporal multiplier networks for video action recognition
C Feichtenhofer, A Pinz, RP Wildes
Proceedings of the IEEE conference on computer vision and pattern …, 2017
7682017
SlowFast Networks for Video Recognition
C Feichtenhofer, H Fan, J Malik, K He
International Conference on Computer Vision (ICCV), 2019, 2019
6742019
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
3382017
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/CVF Conference on Computer Vision and Pattern …, 2019
2862019
Long-term feature banks for detailed video understanding
CY Wu, C Feichtenhofer, H Fan, K He, P Krahenbuhl, R Girshick
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
1942019
X3D: Expanding architectures for efficient video recognition
C Feichtenhofer
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
1032020
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
902013
Temporal Residual Networks for Dynamic Scene Recognition
C Feichtenhofer, A Pinz, RP Wildes
Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, 2017
702017
Bags of Spacetime Energies for Dynamic Scene Recognition
C Feichtenhofer, A Pinz, RP Wildes
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014
622014
Modeling human motion with quaternion-based neural networks
D Pavllo, C Feichtenhofer, M Auli, D Grangier
International Journal of Computer Vision, 1-18, 2019
572019
Audiovisual slowfast networks for video recognition
F Xiao, YJ Lee, K Grauman, J Malik, C Feichtenhofer
arXiv preprint arXiv:2001.08740, 2020
432020
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
422018
Spacetime Forests with Complementary Features for Dynamic Scene Recognition.
C Feichtenhofer, A Pinz, RP Wildes
BMVC, 6, 2013
402013
A multigrid method for efficiently training video models
CY Wu, R Girshick, K He, C Feichtenhofer, P Krahenbuhl
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
382020
Adaptive smoothed online multi-task learning
K Murugesan, H Liu, J Carbonell, Y Yang
Advances in Neural Information Processing Systems, 4296-4304, 2016
322016
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
312016
Dynamically encoded actions based on spacetime saliency
C Feichtenhofer, A Pinz, RP Wildes
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015
282015
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
272014
Grounded human-object interaction hotspots from video
T Nagarajan, C Feichtenhofer, K Grauman
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
262019
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