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Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs.
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2017
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R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma et al. , “Visual genome: Connecting language and vision using crowdsourced dense image annotations,” IJCV , 2017
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2017
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B. Dai, Y. Zhang, and D. Lin, “Detecting visual relationships with deep relational networks,” in Proc. of CVPR , 2017
2017
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H. Zhang, Z. Kyaw, S.-F. Chang, and T.-S. Chua, “Visual translation embedding network for visual relation detection,” in Proc. of CVPR , 2017
2017
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D. Xu, Y. Zhu, C. B. Choy, and L. Fei-Fei, “Scene graph generation by iterative message passing,” in Proc. of CVPR , 2017
2017
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R. Yu, A. Li, V. I. Morariu, and L. S. Davis, “Visual relationship detection with internal and external linguistic knowledge distillation,” in Proc. of ICCV , 2017
2017
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T. Zhang, S. Whitehead, H. Zhang, H. Li, J. Ellis, L. Huang, W. Liu, H. Ji, and S.-F. Chang, “Improving event extraction via multimodal integration,” in Proc. of ACM MM , 2017
2017
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2020
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W. Wang, R. Liu, M. Wang, S. Wang, X. Chang, and Y. Chen, “Memory-based network for scene graph with unbalanced relations,” in Proc. of ACM MM , 2020
2020
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2020
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2020
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2020
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K. Yang, K. Qinami, L. Fei-Fei, J. Deng, and O. Russakovsky, “Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy,” in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , 2020
2020
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Y. Guo, J. Chen, H. Zhang, and Y.-G. Jiang, “Visual relations augmented cross-modal retrieval,” in Proc. of ICMR , 2020
2020
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S. Wang, R. Wang, Z. Yao, S. Shan, and X. Chen, “Cross-modal scene graph matching for relationship-aware image-text retrieval,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2020
2020
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2020
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A. V. Kannan, D. Fradkin, I. Akrotirianakis, T. Kulahcioglu, A. Canedo, A. Roy, S.-Y. Yu, M. Arnav, and M. A. Al Faruque, “Multimodal knowledge graph for deep learning papers and code,” in Proc. of CIKM , 2020
2020
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2020
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L. Chen, Z. Li, Y. Wang, T. Xu, Z. Wang, and E. Chen, “Mmea: Entity alignment for multi-modal knowledge graph,” in Proc. of KSEM , 2020
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2020
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2020
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2021
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C. Deng, Y. Jia, H. Xu, C. Zhang, J. Tang, L. Fu, W. Zhang, H. Zhang, X. Wang, and C. Zhou, “Gakg: A multimodal geoscience academic knowledge graph,” in Proc. of CIKM , 2021
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2021
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W. Zheng, L. Yan, C. Gou, Z.-C. Zhang, J. J. Zhang, M. Hu, and F.-Y. Wang, “Pay attention to doctor-patient dialogues: Multi-modal knowledge graph attention image-text embedding for covid-19 diagnosis,” Information Fusion , 2021
2021
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2021
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D. Chen, Z. Li, B. Gu, and Z. Chen, “Multimodal named entity recognition with image attributes and image knowledge,” in Proc. of DASFAA , 2021
2021
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X. Shen, B. Yi, H. Liu, W. Zhang, Z. Zhang, S. Liu, and N. Xiong, “Deep variational matrix factorization with knowledge embedding for recommendation system,” IEEE Trans. Knowl. Data Eng. , 2021
2021
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2021
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2022
Closest in time.
L. H. Li, P. Zhang, H. Zhang, J. Yang, C. Li, Y. Zhong, L. Wang, L. Yuan, L. Zhang, J.-N. Hwang et al. , “Grounded language-image pre-training,” in Proc. of CVPR , 2022
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P. Wang, A. Yang, R. Men, J. Lin, S. Bai, Z. Li, J. Ma, C. Zhou, J. Zhou, and H. Yang, “Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework,” in Proc. of ICML , 2022
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X. Jiang, A. Li, J. Liang, B. Liu, R. Xie, W. Wu, Z. Li, and Y. Xiao, “Visualizable or non-visualizable? exploring the visualizability of concepts in multi-modal knowledge graph,” in Proc. of DASFAA , 2022
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