Dual ResGCN for Balanced Scene GraphGeneration

• URL: http://arxiv.org/abs/2011.04234v1
• Date: Mon, 9 Nov 2020 07:44:17 GMT
• Title: Dual ResGCN for Balanced Scene GraphGeneration
• Authors: Jingyi Zhang, Yong Zhang, Baoyuan Wu, Yanbo Fan, Fumin Shen and Heng Tao Shen
• Abstract summary: We propose a novel model, dubbed textitdual ResGCN, which consists of an object residual graph convolutional network and a relation residual graph convolutional network. The two networks are complementary to each other. The former captures object-level context information, textiti.e., the connections among objects. The latter is carefully designed to explicitly capture relation-level context information textiti.e., the connections among relations.
• Score: 106.7828712878278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual scene graph generation is a challenging task. Previous works have achieved great progress, but most of them do not explicitly consider the class imbalance issue in scene graph generation. Models learned without considering the class imbalance tend to predict the majority classes, which leads to a good performance on trivial frequent predicates, but poor performance on informative infrequent predicates. However, predicates of minority classes often carry more semantic and precise information~(\textit{e.g.}, \emph{`on'} v.s \emph{`parked on'}). % which leads to a good score of recall, but a poor score of mean recall. To alleviate the influence of the class imbalance, we propose a novel model, dubbed \textit{dual ResGCN}, which consists of an object residual graph convolutional network and a relation residual graph convolutional network. The two networks are complementary to each other. The former captures object-level context information, \textit{i.e.,} the connections among objects. We propose a novel ResGCN that enhances object features in a cross attention manner. Besides, we stack multiple contextual coefficients to alleviate the imbalance issue and enrich the prediction diversity. The latter is carefully designed to explicitly capture relation-level context information \textit{i.e.,} the connections among relations. We propose to incorporate the prior about the co-occurrence of relation pairs into the graph to further help alleviate the class imbalance issue. Extensive evaluations of three tasks are performed on the large-scale database VG to demonstrate the superiority of the proposed method.

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