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Prompt learning has achieved great success in efficiently exploiting large-scale pre-trained models in natural language processing (NLP).
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Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. In: IEEE Conf. Comput. Vis. Pattern Recog., pp. 16816–16825 (2022)
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Li, A., Zhuang, L., Fan, S., Wang, S.: Learning common and specific visual prompts for domain generalization. In: Proceedings of the Asian Conference on Computer Vision, pp. 4260–4275 (2022)
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Cui, G., Hu, S., Ding, N., Huang, L., Liu, Z.: Prototypical verbalizer for prompt-based few-shot tuning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7014–7024 (2022)
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Zhang, Y., Zhou, K., Liu, Z.: Neural prompt search. arXiv preprint arXiv:2206.04673 (2022)
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Huang, Q., Dong, X., Chen, D., Zhang, W., Wang, F., Hua, G., Yu, N.: Diversity-aware meta visual prompting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10878–10887 (2023)
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