Multi-modal Context-Aware Network for Scene Graph Generation

1. Chen, T., et al.: Knowledge-embedded routing network for scene graph generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6163–6171 (2019)

   Google Scholar 

2. Cui, Z., et al.: Context-dependent diffusion network for visual relationship detection. In: Proceedings of the ACM International Conference on Multimedia, pp. 1475–1482 (2018)

   Google Scholar 

3. Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. 123(1), 32–73 (2017)

   Article  MathSciNet  Google Scholar 

4. Lin, X., et al.: GPS-net: graph property sensing network for scene graph generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3746–3753 (2020)

   Google Scholar 

5. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

   Article  Google Scholar 

6. Pennington, J., et al.: GloVe: global vectors for word representation. In: Proceedings of the conference on Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)

   Google Scholar 

7. Qi, M., et al.: Attentive relational networks for mapping images to scene graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3957–3966 (2019)

   Google Scholar 

8. Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, vol. 28, pp. 91–99 (2015)

   Google Scholar 

9. Sharifzadeh, S., et al.: Classification by attention: scene graph classification with prior knowledge. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 5025–5033 (2021)

   Google Scholar 

10. Speer, R., Havasi, C.: ConceptNet 5: a large semantic network for relational knowledge. In: Gurevych, I., Kim, J. (eds.) The People’s Web Meets NLP. TANLP, pp. 161–176. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35085-6_6

    Chapter  Google Scholar 

11. Suhail, M., et al.: Energy-based learning for scene graph generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 13936–13945 (2021)

    Google Scholar 

12. Xu, D., et al.: Scene graph generation by iterative message passing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5410–5419 (2017)

    Google Scholar 

13. Yan, S., et al.: PCPL: predicate-correlation perception learning for unbiased scene graph generation. In: Proceedings of the ACM International Conference on Multimedia, pp. 265–273 (2020)

    Google Scholar 

14. Yang, G., et al.: Probabilistic modeling of semantic ambiguity for scene graph generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12527–12536 (2021)

    Google Scholar 

15. Yun, S., et al.: Graph transformer networks. In: Proceedings of the Advances in Neural Information Processing Systems, vol. 32, pp. 11983–11993 (2019)

    Google Scholar 

16. Zareian, A., Karaman, S., Chang, S.-F.: Bridging knowledge graphs to generate scene graphs. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 606–623. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58592-1_36

    Chapter  Google Scholar 

17. Zellers, R., et al.: Neural motifs: scene graph parsing with global context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5831–5840 (2018)

    Google Scholar 

18. Zhong, Y., et al.: Learning to generate scene graph from natural language supervision. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1823–1834 (2021)

    Google Scholar 

Download references