Learning from the scene and borrowing from the rich: tackling the long tail in scene graph generation

Tao He, Lianli Gao, Jingkuan Song, Jianfei Cai, Yuan Fang Li

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue. Existing methods largely rely on either external knowledge or statistical bias information to alleviate this problem. In this paper, we tackle this issue from another two aspects: (1) scene-object interaction aiming at learning specific knowledge from a scene via an additive attention mechanism; and (2) long-tail knowledge transfer which tries to transfer the rich knowledge learned from the head into the tail. Extensive experiments on the benchmark dataset Visual Genome on three tasks demonstrate that our method outperforms current state-of-the-art competitors. Our source code is available at https://github.com/htlsn/issg.

Original languageEnglish
Title of host publicationProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
EditorsChristian Bessiere
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages587-593
Number of pages7
ISBN (Electronic)9780999241165
DOIs
Publication statusPublished - 2020
EventInternational Joint Conference on Artificial Intelligence-Pacific Rim International Conference on Artificial Intelligence 2020 - Yokohama, Japan
Duration: 7 Jan 202115 Jan 2021
Conference number: 29th/17th
https://www.ijcai.org/Proceedings/2020/ (Proceedings)
https://ijcai20.org (Website)

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2021-January
ISSN (Print)1045-0823

Conference

ConferenceInternational Joint Conference on Artificial Intelligence-Pacific Rim International Conference on Artificial Intelligence 2020
Abbreviated titleIJCAI-PRICAI 2020
Country/TerritoryJapan
CityYokohama
Period7/01/2115/01/21
OtherIJCAI-PRICAI 2020, the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence!IJCAI-PRICAI2020 will take place January 7-15, 2021 online in a virtual reality in Japanese Standard Time (JST) zone.
Internet address

Keywords

  • Computer Vision
  • Recognition
  • Detection
  • Categorization
  • Indexing
  • Matching
  • Retrieval
  • Semantic Interpretation
  • Machine Learning
  • Deep Learning

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