Improving Sequential Determinantal Point Processes for Supervised Video Summarization

Aidean Sharghi , Ali Borji, Chengtao Li , Tianbao Yang , Boqing Gong; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 517-533

Abstract


It is now much easier than ever before to produce videos. While the ubiquitous video data is a great source for information discovery and extraction, the computational challenges are unparalleled. Automatically summarizing the videos has become a substantial need for browsing, searching, and indexing visual content. This paper is in the vein of supervised video summarization using sequential determinantal point processes (SeqDPPs), which models diversity by a probabilistic distribution. We improve this model in two folds. In terms of learning, we propose a large-margin algorithm to address the exposure bias problem in SeqDPP. In terms of modeling, we design a new probabilistic distribution such that, when it is integrated into SeqDPP, the resulting model accepts user input about the expected length of the summary. Moreover, we also significantly extend a popular video summarization dataset by 1) more egocentric videos, 2) dense user annotations, and 3) a refined evaluation scheme. We conduct extensive experiments on this dataset (about 60 hours of videos in total) and compare our approach to several competitive baselines.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Sharghi_2018_ECCV,
author = {Sharghi, Aidean and Borji, Ali and Li, Chengtao and Yang, Tianbao and Gong, Boqing},
title = {Improving Sequential Determinantal Point Processes for Supervised Video Summarization},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}