DAPs Deep Action Proposals for Action Understanding.pdf
Supplementary material 1
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2016-09-17
Print Publication Date2016
Permanent link to this recordhttp://hdl.handle.net/10754/604944
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AbstractObject 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.
CitationEscorcia V., Caba Heilbron F., Niebles J.C., Ghanem B. (2016) DAPs: Deep Action Proposals for Action Understanding. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9907. Springer, Cham
SponsorsResearch 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).
Conference/Event nameComputer Vision – ECCV 2016