Continuous Self-study: Scene Graph Generation with Self-knowledge Distillation and Spatial Augmentation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-26348-4_18
Reference69 articles.
1. Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.: Scene graph generation by iterative message passing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5410–5419 (2017)
2. Zellers, R., Yatskar, M., Thomson, S., Choi, Y.: Neural motifs: scene graph parsing with global context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5831–5840 (2018)
3. Ye, K., Kovashka, A.: Linguistic structures as weak supervision for visual scene graph generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8289–8299 (2021)
4. Wang, Y.S., Liu, C., Zeng, X., Yuille, A.: Scene graph parsing as dependency parsing. arXiv preprint arXiv:1803.09189 (2018)
5. Marino, K., Salakhutdinov, R., Gupta, A.: The more you know: using knowledge graphs for image classification. arXiv preprint arXiv:1612.04844 (2016)
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