Novel-Registrable Weights and Region-Level Contrastive Learning for Incremental Few-shot Object Detection

Author:

Tang Shiyuan,Mei Hefei,Qiu Heqian,Hao Xinpeng,Zhao Taijin,Qiu Benliu,Cheng Haoyang,Jiao Jian,Gong Chuanyang,Li Hongliang

Publisher

Springer Nature Singapore

Reference32 articles.

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2. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

3. Chen, X., He, K.: Exploring simple Siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15750–15758 (2021)

4. Cheng, M., Wang, H., Long, Y.: Meta-learning-based incremental few-shot object detection. IEEE Trans. Circuits Syst. Video Technol. 32(4), 2158–2169 (2022). https://doi.org/10.1109/TCSVT.2021.3088545

5. Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., Brox, T.: Discriminative unsupervised feature learning with convolutional neural networks. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc. (2014)

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