DAPs: Deep Action Proposals for Action Understanding

DAPs: Deep Action Proposals for Action Understanding

Victor Escorcia, Fabian Caba, Juan Carlos Niebles, Bernard Ghanem
"DAPs: Deep Action Proposals for Action Understanding"
European Conference on Computer Vision (ECCV 2016)
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Victor Escorcia, Fabian Caba, Juan Carlos Niebles, Bernard Ghanem
action proposals, action detection, long-short term memory
2016
​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. ​