Hilde Kuehne
Hilde Kuehne
Goethe University Frankfurt , MIT-IBM Watson Lab
Verified email at em.uni-frankfurt.de - Homepage
Cited by
Cited by
HMDB: a large video database for human motion recognition
H Kuehne, H Jhuang, E Garrote, T Poggio, T Serre
2011 International conference on computer vision, 2556-2563, 2011
The language of actions: Recovering the syntax and semantics of goal-directed human activities
H Kuehne, A Arslan, T Serre
Proceedings of the IEEE conference on computer vision and pattern …, 2014
Weakly supervised action learning with rnn based fine-to-coarse modeling
A Richard, H Kuehne, J Gall
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
An end-to-end generative framework for video segmentation and recognition
H Kuehne, J Gall, T Serre
2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 1-8, 2016
Weakly supervised learning of actions from transcripts
H Kuehne, A Richard, J Gall
Computer Vision and Image Understanding 163, 78-89, 2017
Neuralnetwork-viterbi: A framework for weakly supervised video learning
A Richard, H Kuehne, A Iqbal, J Gall
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
Hmm-based human motion recognition with optical flow data
D Gehrig, H Kuehne, A Woerner, T Schultz
2009 9th IEEE-RAS International Conference on Humanoid Robots, 425-430, 2009
Combined intention, activity, and motion recognition for a humanoid household robot
D Gehrig, P Krauthausen, L Rybok, H Kuehne, UD Hanebeck, T Schultz, ...
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2011
Action sets: Weakly supervised action segmentation without ordering constraints
A Richard, H Kuehne, J Gall
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
More is less: Learning efficient video representations by big-little network and depthwise temporal aggregation
Q Fan, CF Chen, H Kuehne, M Pistoia, D Cox
arXiv preprint arXiv:1912.00869, 2019
Unsupervised learning of action classes with continuous temporal embedding
A Kukleva, H Kuehne, F Sener, J Gall
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
A hybrid RNN-HMM approach for weakly supervised temporal action segmentation
H Kuehne, A Richard, J Gall
IEEE transactions on pattern analysis and machine intelligence 42 (4), 765-779, 2018
Extraction and analysis of coronary tree from single X-ray angiographies
H Koehler, M Couprie, S Bouattour, D Paulus
Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display …, 2004
Automatic human model parametrization from 3d marker data for motion recognition
T Koehler, M Pruzinec, T Feldmann, A Wörner
Proceedings of WSCG, 2008
On-line Action Recognition from Sparse Feature Flow.
H Kuehne, D Gehrig, T Schultz, R Stiefelhagen
VISAPP (1), 634-639, 2012
Superficial Gaussian Mixture Reduction.
MF Huber, P Krauthausen, UD Hanebeck
GI-Jahrestagung, 491, 2011
Mining youtube-a dataset for learning fine-grained action concepts from webly supervised video data
H Kuehne, A Iqbal, A Richard, J Gall
arXiv preprint arXiv:1906.01012, 2019
An iterative scheme for motion-based scene segmentation
A Bachmann, H Kuehne
2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV …, 2009
Joint visual-temporal embedding for unsupervised learning of actions in untrimmed sequences
RG VidalMata, WJ Scheirer, A Kukleva, D Cox, H Kuehne
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021
Towards a generative approach to activity recognition and segmentation
H Kuehne, T Serre
CoRR, vol. abs/1509.01947, 2015
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