@inproceedings{15c6e70c8107487e92a819f24c565007,
title = "Relation-Aware Reasoning with Graph Convolutional Network",
abstract = "Semantic dependencies among objects are crucial for the recognition system to enhance performance. However, utilizing object-object relationships is a non-trivial task as objects are of various scales and locations, leading to irregular relationships. In this paper, we present a novel visual reasoning framework that incorporates both semantic and spatial relationships to improve the recognition system. We at first construct a knowledge graph to represent the co-occurrence frequency and relative position among categories. Based on this knowledge graph, we are able to enhance the original regional features by a Graph Convolutional Network (GCN) that encodes the high-level semantic contexts. Experiments show that our framework manages to outperform the baselines and state-of-the-art on different backbones in terms of both per-instance and per-class classification accuracy.",
keywords = "Graph Convolutional Network, Knowledge graph, Object-object relationship, Visual reasoning",
author = "Lei Zhou and Yang Liu and Xiao Bai and Xiang Wang and Chen Wang and Liang Zhang and Lin Gu",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 11th International Conference on Image and Graphics, ICIG 2021 ; Conference date: 06-08-2021 Through 08-08-2021",
year = "2021",
doi = "10.1007/978-3-030-87355-4\_5",
language = "英语",
isbn = "9783030873547",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "52--64",
editor = "Yuxin Peng and Shi-Min Hu and Moncef Gabbouj and Kun Zhou and Michael Elad and Kun Xu",
booktitle = "Image and Graphics - 11th International Conference, ICIG 2021, Proceedings",
address = "德国",
}