---
layout: post
title:  DAPs&#58; Deep Action Proposals for Video Understanding
date:   2016-04-8 00:00:00 -0500
img: img/portfolio/kaust16.png
img_modal: img/portfolio/kaust16_modal.png
modalID: cvDaps2016
category: Computer Vision
---
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.

[Paper](#), [Video](https://drive.google.com/open?id=0B0ZXjo_p8lHBRGlnZ0Q1dFk2SUk), Code (comming soon)

---
layout: post
title:  DAPs&#58; Deep Action Proposals for Video Understanding
date:   2016-04-8 00:00:00 -0500
img: img/portfolio/kaust16.png
img_modal: img/portfolio/kaust16_modal.png
modalID: cvDaps2016
category: Computer Vision
---
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.

[Paper](https://drive.google.com/open?id=0B0ZXjo_p8lHBVm1ySm1QZHN5dWc), [Video](https://drive.google.com/open?id=0B0ZXjo_p8lHBRGlnZ0Q1dFk2SUk), Code (comming soon)

Update link to kaust16 paper
[post] Update link to kaust16 paper
