Top Attention in Line with Time: A Light-weight Strategy

This repository is the source code of "Top Attention". The code is tested on the Ubuntu 14.04 and matlab 2014a.

Requirments

• System: Ubuntu 14.04
• Matlab 2014a
• vlfeat 0.9.20
• caffe

pretrained model

• TODO

running

• ./extract_ucf_ldd_res.m : extract the 'Line-pooled Deep-convolutional Descriptors (LDD)'
• ./extract_ucf_ldd_tw_topweight_res.m : extract the descriptors and the 'top weights'
• ./vlad/vl_main.m : run 'Vlad' with LDD
• ./vlad_w/vl_main.m : run 'Weight-Vlad' with LDD

Examples

• Visualization of Top Attention. The LDDs from the bright regions will be reserved, while others will be filtered. We can find that most removed LDDs belong to background.

• Comparison of VLAD and Weight-VLAD. The left column shows the original images. The middle column shows that VLAD aggregates the differences between reserved descriptors and the corresponding centers. The right column shows that the Weight-VLAD can focus on the key regions.

• Visualizations of Top Attention. The brighter a region is, the more salient a motion of action is along the time line.

Paper

Youjiang Xu, Shichao Zhao, Yahong Han, Qinghua Hu, Fei Wu. "Top Attention in Line with Time: A Light-weight Strategy". ICME. 2017.

@inproceedings{xu2017top,
  title={Top attention in line with time: A light-weight strategy},
  author={Xu, Youjiang and Zhao, Shichao and Han, Yahong and Hu, Qinghua and Wu, Fei},
  booktitle={Multimedia and Expo (ICME), 2017 IEEE International Conference on},
  pages={295--300},
  year={2017},
  organization={IEEE}
}