Abstract
For better understanding an image, the relationships between objects can provide valuable spatial information and semantic clues besides recognition of all objects. However, current scene graph generation methods don’t effectively exploit the latent visual information in relationships. To dig a better relationship hidden in visual content, we design a node-relation context module for scene graph generation. Firstly, GRU hidden states of the nodes and the edges are used to guide the attention of subject and object regions. Then, together with the hidden states, the attended visual features are fed into a fusion function, which can obtain the final relationship context. Experimental results manifest that our method is competitive with the current methods on Visual Genome dataset.
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Acknowledgement
This work was partially supported by National Natural Science Foundation of China (NSFC Grant No. 61773272, 61272258, 61301299, 61572085, 61272005), Science and Education Innovation based Cloud Data fusion Foundation of Science and Technology Development Center of Education Ministry (2017B03112), Six talent peaks Project in Jiangsu Province (DZXX-027), Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University (Grant No. 93K172016K08), and Provincial Key Laboratory for Computer Information Processing Technology, Soochow University.
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Lin, X., Li, Y., Liu, C., Ji, Y., Yang, J. (2018). Scene Graph Generation Based on Node-Relation Context Module. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_12
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DOI: https://doi.org/10.1007/978-3-030-04179-3_12
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