DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization

Safa Messaoud, Ismini Lourentzou, Assma Boughoula, Mona Zehni, Zhizhen Zhao, Chengxiang Zhai, Alexander G. Schwing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content. Query-aware multi-video summarization is a promising technique that caters to this demand. In this work, we introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS, that jointly optimizes multiple criteria: (1) conciseness, (2) representativeness of important query-relevant events and (3) chronological soundness. We design a hierarchical attention model that factorizes over three distributions, each collecting evidence from a different modality, followed by a pointer network that selects frames to include in the summary. DeepQAMVS is trained with reinforcement learning, incorporating rewards that capture representativeness, diversity, query-adaptability and temporal coherence. We achieve state-of-the-art results on the MVS1K dataset, with inference time scaling linearly with the number of input video frames.

Original languageEnglish (US)
Title of host publicationSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages1389-1399
Number of pages11
ISBN (Electronic)9781450380379
DOIs
StatePublished - Jul 11 2021
Event44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021 - Virtual, Online, Canada
Duration: Jul 11 2021Jul 15 2021

Publication series

NameSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021
Country/TerritoryCanada
CityVirtual, Online
Period7/11/217/15/21

Keywords

  • hierarchical attention
  • multi-video summarization
  • pointer networks
  • reinforcement learning
  • video summarization

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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