Tubelets: Unsupervised Action Proposals from Spatiotemporal Super-Voxels
Author:
Funder
University of Amsterdam
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
http://link.springer.com/article/10.1007/s11263-017-1023-9/fulltext.html
Reference76 articles.
1. Brox, T., & Malik, J. (September 2010). Object segmentation by long term analysis of point trajectories. In Proceedings of the European conference on computer vision.
2. Cao, L., Liu, Z., & Huang, T. S. (June 2010). Cross-dataset action detection. In Proceedings of the IEEE conference on computer vision and pattern recognition.
3. Chen, W., & Corso, J. J. (2015). Action detection by implicit intentional motion clustering. In Proceedings of the IEEE international conference on computer vision.
4. Chen, W., Xiong, C., Xu, R., & Corso, J. (2014). Actionness ranking with lattice conditional ordinal random fields. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 748–755).
5. Cheron, G., Laptev, I., & Schmid, C. (December 2015). P-cnn: Pose-based cnn features for action recognition. In Proceedings of the IEEE international conference on computer vision.
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