.png){kind=icon}
.png){kind=icon}
Return
Meta Learning for Task-Driven Video Summarization
.png){kind=icon}
OA
AI
DOI：10.1109/TIE.2019.2931283.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Existing video summarization approaches mainly concentrate on the sequential or structural characteristic of video data. However, they do not pay enough attention to the video summarization task itself. In this article, we propose a meta learning method for performing task-driven video summarization, denoted by MetaL-TDVS, to explicitly explore the video summarization mechanism among summarizing processes on different videos. Particularly, MetaL-TDVS aims to excavate the latent mechanism for summarizing video by reformulating video summarization as a meta learning problem and promote the generalization ability of the trained model. MetaL-TDVS regards summarizing each video as a single task to make better use of the experience and knowledge learned from processes of summarizing other videos to summarize new ones. Furthermore, MetaL-TDVS updates models via a twofold backpropagation, which forces the model optimized on one video to obtain high accuracy on another video in every training step. Extensive experiments on benchmark datasets demonstrate the superiority and better generalization ability of MetaL-TDVS against several state-of-the-art methods.
Keywords:
Task analysis
Training
Metals
Streaming media
Computational modeling
Data models
Decoding
Keyframe extraction
meta learning
video summarization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.2
Papers:
1.8W
Citations:
9.8W