Exploring and exploiting the hierarchical structure of a scene for scene graph generation

研究成果: Conference contribution

抄録

The scene graph of an image is an explicit, concise representation of the image; hence, it can be used in various applications such as visual question answering or robot vision. We propose a novel neural network model for generating scene graphs that maintain global consistency, which prevents the generation of unrealistic scene graphs; the performance in the scene graph generation task is expected to improve. Our proposed model is used to construct a hierarchical structure whose leaf nodes correspond to objects depicted in the image, and a message is passed along the estimated structure on the fly. To this end, we aggregate features of all objects into the root node of the hierarchical structure, and the global context is back-propagated to the root node to maintain all the object nodes. The experimental results on the Visual Genome dataset indicate that the proposed model outperformed the existing models in scene graph generation tasks. We further qualitatively confirmed that the hierarchical structures captured by the proposed model seemed to be valid.

本文言語English
ホスト出版物のタイトルProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1422-1429
ページ数8
ISBN(電子版)9781728188089
DOI
出版ステータスPublished - 2020
イベント25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
継続期間: 2021 1 102021 1 15

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
ISSN(印刷版)1051-4651

Conference

Conference25th International Conference on Pattern Recognition, ICPR 2020
国/地域Italy
CityVirtual, Milan
Period21/1/1021/1/15

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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