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Atom correlation based graph propagation for scene graph generation
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NSTL
Elsevier
Long-tailed distribution in the dataset is one of the major problems of the scene graph generation task. Previous methods attempt to alleviate this by introducing human commonsense knowledge in the form of statistical correlations between object pairs. However, the reasoning path they used is usually composable and the prior knowledge they employed is generally image-specific, making the knowledge learning less flexible, stable and holistic. In this paper, we propose Atom Correlation Based Graph Propagation (AC GP) for the scene graph generation task. Specifically, diverse atom correlations between objects and their relationships are explored by separating relationships to form new semantic nodes and decomposing the compound reasoning paths. Based on these atom correlations, the knowledge graphs are introduced for the feature enhancement by information propagating in the global category space. By exploiting atom correlations, the introduced prior knowledge can be more common and easy to learn. Moreover, propagating the knowledge in the global category space enables the model aware of more comprehensive and holistic knowledge. As a result, the model capacity and stability can be effectively im proved to mine infrequent and missed relationships. Experimental results on two benchmark datasets: Visual Relation Detection (VRD) and Visual Genome (VG) show the superiority of the proposed AC-GP over strong baseline methods. (c) 2021 Elsevier Ltd. All rights reserved.
Scene graph generationLong-tailed distributionKnowledge graphAtom correlationCategory space
NSTL
Elsevier
