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PHD<sup>2</sup>: Personalized Highlight Detection Dataset

This repository contains PHD<sup>2</sup>, a dataset with personalized highlight information. As such, it allows to train highlight detection models that use information about the user, when making predictions. The dataset contains information from more than 13'000 users.

Motivation

Highlight detection algorithms automatically reduce a video to its most interesting moments. Even though this notion of interestingness is subjective, existing works only learn generic highlight models, which limits their potential performance. To achieve better performance, models need to rely on information about the user for which it should extract highlights.

Previously, such data was unavailable in the research community, which prevented the research on personalized highlight detection models. Thus, at gifs.com, we created a large-scale dataset of users and the GIFs they created, which provides an accurate indication of their interests. On this data, models can be trained to make personalized, and thus more accurate, predictions. Indeed, as we show in our paper [1], making models personalized improves the performance over generic highlight detection models such as [2] by a significant margin.

We are happy to publicly release this dataset to the research community and hope that it will spur research on personalized highlight detection.

Dataset Description

The dataset contains information on what video segments a specific user considers a highlight. Having this kind of data allows for strong personalization models, as specific examples of what a user is interested in help models obtain a fine-grained understanding of that specific user.

The data consists of YouTube videos, from which gifs.com users manually extracted their highlights, by creating GIFs from a segment of the full video. Thus, the dataset is similar to that of [1], with two major differences.

  1. Each selection is associated with a user, which is what allows personalization.
  2. [1] used visual matching to find the position in the video from which a GIF was selected. Instead, we directly use the timestamps, which we have internally available. Thus, the ground truth is free from any alignment errors.

Training set The training set contains highlights from 12'972 users. You can find it here.

Test set The test set contains highlights from 850 users. You can find it here.

Dataset example

example users in our dataset

More information

For more information on the dataset, see our paper [1].

Using the dateset

Data format

The data is stored in csv files. They have the following fields:

youtubeId       the source youtube video
start           start of the highlight selection (in seconds)
duration        duration of the selection (in seconds)
user_id         the ID of that user
video_duration  the full video duration
is_last         is this video the last one the user selected highlights from? (boolean)

Most fields should be self-explanatory. The is_last field indicates if that particular video was the last the user extracted highlights from. This information is needed for predicting the highlights for a user, given his previously created GIFs.

To make this more clear, consider this user (id 12999) from the test set:

youtubeId,start,duration,user_id,video_duration,is_last
dhuI9TO1rY8,204.39,7.08,12999,314.0,True
dhuI9TO1rY8,206.29,5.29,12999,314.0,True
Z-aTqtRBTIM,0.0,7.66,12999,8.0,False
An7ylfOhlOs,82.99,7.55,12999,133.0,False
EFweWyBUFk4,238.95,7.13,12999,561.0,False
2GSS6rSn5Gk,384.05,6.91,12999,1027.0,False
WSEsOJQJ990,0.0,2.08,12999,3.0,False
H9tPfQNXFy8,128.607,8.75021,12999,143.0,False

There is one video (2 rows) which has is_last==True. Thus, a highlight detection model that uses information about the user would predict the users highlight selection on this video:

youtubeId,start,duration,user_id,video_duration,is_last
dhuI9TO1rY8,204.39,7.08,12999,314.0,True
dhuI9TO1rY8,206.29,5.29,12999,314.0,True

by using the information on the previous video segments the user selected:

youtubeId,start,duration,user_id,video_duration,is_last
Z-aTqtRBTIM,0.0,7.66,12999,8.0,False
An7ylfOhlOs,82.99,7.55,12999,133.0,False
EFweWyBUFk4,238.95,7.13,12999,561.0,False
2GSS6rSn5Gk,384.05,6.91,12999,1027.0,False
WSEsOJQJ990,0.0,2.08,12999,3.0,False
H9tPfQNXFy8,128.607,8.75021,12999,143.0,False

Downloading the videos

For using the dataset, you need to first get the videos from YouTube. We recommend using pafy or youtube-dl for this.

Evaluation metrics

We encourage using mAP, nMSD and Recall as the performance metrics. For reference, our model obtains mAP=16.68%, nMSD=40.26% and Recall@5=30.71%, while the public model of [1] obtains mAP=15.69%, nMSD=42.59% and Recall@5=27.28% on the 850 test videos which have is_last==True. You can find the implementation on mAP and nMSD here.

Citation

If you use this dataset, please cite our paper:

@inproceedings{gifs2017highlights,
   author = {Garcia del Molino, Ana and Gygli, Michael},
   title = {{PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation}},
   booktitle = {Proceedings of the 2018 ACM on Multimedia Conference},
   series = {MM '18},
   year = {2018},
   publisher = {ACM},
   address = {New York, NY, USA}
}

References

[1] PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation. A. García del Molino, M. Gygli. ACM Multimedia 2018. https://arxiv.org/abs/1804.06604

[2] Video2GIF: Automatic Generation of Animated GIFs from Video. M. Gygli, Y. Song, L. Cao. CVPR 2016 https://arxiv.org/abs/1605.04850