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Christoph Feichtenhofer
Christoph Feichtenhofer
Research Scientist, Facebook AI Research (FAIR)
Verified email at fb.com - Homepage
Title
Cited by
Cited by
Year
Convolutional two-stream network fusion for video action recognition
C Feichtenhofer, A Pinz, AP Zisserman
Computer Vision and Pattern Recognition (CVPR), 2016, 2016
24572016
SlowFast Networks for Video Recognition
C Feichtenhofer, H Fan, J Malik, K He
International Conference on Computer Vision (ICCV), 2019, 2019
12092019
Spatiotemporal Residual Networks for Video Action Recognition
C Feichtenhofer, A Pinz, RP Wildes
Advances in Neural Information Processing Systems, 3468-3476, 2016
903*2016
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
4882019
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
4402017
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
3032019
X3D: Expanding architectures for efficient video recognition
C Feichtenhofer
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
2882020
Multiscale Vision Transformers
H Fan, B Xiong, K Mangalam, Y Li, Z Yan, J Malik, C Feichtenhofer
arXiv preprint arXiv:2104.11227, 2021
1342021
Trackformer: Multi-object tracking with transformers
T Meinhardt, A Kirillov, L Leal-Taixe, C Feichtenhofer
arXiv preprint arXiv:2101.02702, 2021
992021
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
982013
Modeling human motion with quaternion-based neural networks
D Pavllo, C Feichtenhofer, M Auli, D Grangier
International Journal of Computer Vision 128 (4), 855-872, 2020
972020
Audiovisual slowfast networks for video recognition
F Xiao, YJ Lee, K Grauman, J Malik, C Feichtenhofer
arXiv preprint arXiv:2001.08740, 2020
842020
Temporal Residual Networks for Dynamic Scene Recognition
C Feichtenhofer, A Pinz, RP Wildes
Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, 2017
762017
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
682020
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
682014
A large-scale study on unsupervised spatiotemporal representation learning
C Feichtenhofer, H Fan, B Xiong, R Girshick, K He
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
602021
A ConvNet for the 2020s
Z Liu, H Mao, CY Wu, C Feichtenhofer, T Darrell, S Xie
arXiv preprint arXiv:2201.03545, 2022
532022
Grounded human-object interaction hotspots from video
T Nagarajan, C Feichtenhofer, K Grauman
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
512019
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
502018
Spacetime Forests with Complementary Features for Dynamic Scene Recognition.
C Feichtenhofer, A Pinz, RP Wildes
BMVC, 6, 2013
432013
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