Unbiased scene graph generation using the self-distillation method-Reference-Cited by-同舟云学术

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Unbiased scene graph generation using the self-distillation method

Published:2023-06-22 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Sun Bo,Hao Zhuo,Yu Lejun,He Jun

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Zhuhai Science and Technology Planning Project

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s00371-023-02924-9.pdf

Reference28 articles.

1. Zellers, R., Yatskar, M., Thomson, S., Choi, Y.: Neural motifs: scene graph parsing with global context (2018)

2. Tang, K., Niu, Y., Huang, J., Shi, J., Zhang, H.: Unbiased scene graph generation from biased training. In: Conference on Computer Vision and Pattern Recognition (2020)

3. Yu, J., Chai, Y., Hu, Y., Wu, Q.: Cogtree: cognition tree loss for unbiased scene graph generation. In: IJCAI (2021)

4. Yan, S., Shen, C., Jin, Z., Huang, J., Jiang, R., Chen, Y., Hua, X.: Pcpl: Predicate-correlation perception learning for unbiased scene graph generation. In: Proceedings of the 28th ACM International Conference on Multimedia (2020)

5. Chen, D., Liang, X., Wang, Y., Gao, W.: Soft transfer learning via gradient diagnosis for visual relationship detection. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1118–1126 (2019)

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