TY - GEN
T1 - DAPs: Deep Action Proposals for Action Understanding
AU - Escorcia, Victor
AU - Caba Heilbron, Fabian
AU - Niebles, Juan Carlos
AU - Ghanem, Bernard
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Research in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research, the Stanford AI Lab-Toyota Center for Artificial Intelligence Research and a Google Faculty Research Award (2015).
PY - 2016/9/17
Y1 - 2016/9/17
N2 - Object proposals have contributed significantly to recent advances in object understanding in images. Inspired by the success of this approach, we introduce Deep Action Proposals (DAPs), an effective and efficient algorithm for generating temporal action proposals from long videos. We show how to take advantage of the vast capacity of deep learning models and memory cells to retrieve from untrimmed videos temporal segments, which are likely to contain actions. A comprehensive evaluation indicates that our approach outperforms previous work on a large scale action benchmark, runs at 134 FPS making it practical for large-scale scenarios, and exhibits an appealing ability to generalize, i.e. to retrieve good quality temporal proposals of actions unseen in training.
AB - Object proposals have contributed significantly to recent advances in object understanding in images. Inspired by the success of this approach, we introduce Deep Action Proposals (DAPs), an effective and efficient algorithm for generating temporal action proposals from long videos. We show how to take advantage of the vast capacity of deep learning models and memory cells to retrieve from untrimmed videos temporal segments, which are likely to contain actions. A comprehensive evaluation indicates that our approach outperforms previous work on a large scale action benchmark, runs at 134 FPS making it practical for large-scale scenarios, and exhibits an appealing ability to generalize, i.e. to retrieve good quality temporal proposals of actions unseen in training.
UR - http://hdl.handle.net/10754/604944
UR - http://link.springer.com/chapter/10.1007/978-3-319-46487-9_47
UR - http://www.scopus.com/inward/record.url?scp=84990053003&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46487-9_47
DO - 10.1007/978-3-319-46487-9_47
M3 - Conference contribution
SN - 9783319464862
SP - 768
EP - 784
BT - Lecture Notes in Computer Science
PB - Springer Nature
ER -