中文版 | English
Title

Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward

Author
Corresponding AuthorFeng Zheng
DOI
Publication Years
2022-09-25
Conference Name
Asian Conference on Computer Vision2022
Conference Date
2022/12/4-2022/12/8
Conference Place
Macau
Abstract

Advertisement video editing aims to automatically edit advertising videos into shorter videos while retaining coherent content and crucial information conveyed by advertisers. It mainly contains two stages: video segmentation and segment assemblage. The existing method performs well at video segmentation stages but suffers from the problems of dependencies on extra cumbersome models and poor performance at the segment assemblage stage. To address these problems, we propose M-SAN (Multi-modal Segment Assemblage Network) which can perform efficient and coherent segment assemblage task end-to-end. It utilizes multi-modal representation extracted from the segments and follows the Encoder-Decoder Ptr-Net framework with the Attention mechanism. Importance-coherence reward is designed for training M-SAN. We experiment on the Ads-1k dataset with 1000+ videos under rich ad scenarios collected from advertisers. To evaluate the methods, we propose a unified metric, Imp-Coh@Time, which comprehensively assesses the importance, coherence, and duration of the outputs at the same time. Experimental results show that our method achieves better performance than random selection and the previous method on the metric. Ablation experiments further verify that multi-modal representation and importance-coherence reward significantly improve the performance. Ads-1k dataset is available at: https://github.com/yunlong10/Ads-1k

Keywords
SUSTech Authorship
First ; Corresponding
Language
English
Data Source
人工提交
Publication Status
在线出版
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/415607
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Southern University of Science and Technology, China
2.Tencent Inc., China
First Author AffilicationSouthern University of Science and Technology
Corresponding Author AffilicationSouthern University of Science and Technology
First Author's First AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Yunlong Tang,Siting Xu,Teng Wang,et al. Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward[C],2022.
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