Multiple knowledge embedding for few-shot object detection
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-022-02438-2.pdf
Reference30 articles.
1. Chen, P.-Y., Chang, M.-C., Hsieh, J.-W., Chen, Y.-S.: Parallel residual bi-fusion feature pyramid network for accurate single-shot object detection. IEEE Trans. Image Process. 30, 9099–9111 (2021)
2. Tang, J., Shu, X., Li, Z., Qi, G.-J., Wang, J.: Generalized deep transfer networks for knowledge propagation in heterogeneous domains. ACM Trans. Multimed. Comput. Commun. Appl. 12(4s) (2016)
3. Shu, X., Qi, G.-J., Tang, J., Wang, J.: Weakly-shared deep transfer networks for heterogeneous-domain knowledge propagation. In Proceedings of the 23rd ACM International Conference on Multimedia, MM’15, pp. 35–44 (2015)
4. Xiong, C., Li, W., Liu, Y., Wang, M.: Multi-dimensional edge features graph neural network on few-shot image classification. IEEE Signal Process. Lett. 28, 573–577 (2021)
5. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)
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