Tutorial: Open scene graph A: introduction tutorial: Open scene graph B: examples and applications

Subgraph and object context-masked network for scene graph generation

IET Computer Vision ◽

10.1049/iet-cvi.2019.0896 ◽

2020 ◽

Vol 14 (7) ◽

pp. 546-553

Author(s):

Zhenxing Zheng ◽

Zhendong Li ◽

Gaoyun An ◽

Songhe Feng

Keyword(s):

Scene Graph ◽

Graph Generation

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Scene graph adapter

Proceedings of the 16th International Conference on 3D Web Technology - Web3D '11 ◽

10.1145/2010425.2010429 ◽

2011 ◽

Cited By ~ 4

Author(s):

Rozenn Bouville Berthelot ◽

Jérôme Royan ◽

Thierry Duval ◽

Bruno Arnaldi

Keyword(s):

Scene Graph

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Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10070488 ◽

2021 ◽

Vol 10 (7) ◽

pp. 488

Author(s):

Peng Li ◽

Dezheng Zhang ◽

Aziguli Wulamu ◽

Xin Liu ◽

Peng Chen

Keyword(s):

Remote Sensing ◽

Scene Understanding ◽

Deep Understanding ◽

Remote Sensing Images ◽

Convolutional Network ◽

Scene Graph ◽

Multi Scale ◽

Relationship Extraction ◽

High Level ◽

Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

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