[40] Pramuditha Perera, Vlad I. Morariu, Rajiv Jain, Varun Manjunatha, Curtis Wigington, Vicente Ordonez, and Vishal M. Patel. Generative-discriminative feature representations for open-set recognition. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11811-11820, 2020. 1, 3
[41] Rui Qian, Yeqing Li, Zheng Xu, Ming-Hsuan Yang, Serge Belongie, and Yin Cui. Multimodal open-vocabulary video classification via pre-trained vision and language models. arXiv preprint arXiv:2207.07646, 2022. 2
[42] Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. Is chatgpt a general-purpose natural language processing task solver? arXiv preprint arXiv:2302.06476, 2023. 2
[43] Haoxuan Qu, Lin Geng Foo, Yanchao Li, and Jun Liu. Towards more reliable confidence estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 10
[44] Haoxuan Qu, Yanchao Li, Lin Geng Foo, Jason Kuen, Jiumiang Gu, and Jun Liu. Improving the reliability for confidence estimation. In European Conference on Computer Vision, pages 391-408. Springer, 2022. 10
[45] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 2, 3, 6
[46] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 8
[47] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International Conference on Machine Learning, pages 8821-8831. PMLR, 2021. 2, 3, 5
[48] Walter J. Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E. Boult. Toward open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7):1757-1772, 2013. 3
[49] Vikash Sehwag, Mung Chiang, and Prateek Mittal. Ssd: A unified framework for self-supervised outlier detection. In International Conference on Learning Representations, 2020. 3
[50] Lei Shu, Hu Xu, and Bing Liu. Doc: Deep open classification of text documents. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2911-2916, 2017. 3
[51] Yu Shu, Yemin Shi, Yaowei Wang, Tiejun Huang, and Yonghong Tian. P-odn: Prototype-based open deep network for open set recognition. Scientific reports, 10(1):7146, 2020. 3
[52] Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, and Xipeng Qiu. Black-box tuning for language-model-as-a-service. In International Conference on Machine Learning, pages 20841-20855. PMLR, 2022. 10
[53] Xin Sun, Zhenning Yang, Chi Zhang, Keck-Voon Ling, and Guohao Peng. Conditional gaussian distribution learning for open set recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13480-13489, 2020. 3
[54] Niko Sunderhauf, Oliver Brock, Walter Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford, et al. The limits and potentials of deep learning for robotics. The International journal of robotics research, 37(4-5):405-420, 2018. 1
[55] Tuan Truong, Sadegh Mohammadi, and Matthias Lenga. How transferable are self-supervised features in medical image classification tasks? In Machine Learning for Health, pages 54-74. PMLR, 2021. 2
[56] Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman. Open-set recognition: a good closed-set classifier is all you need? In International Conference on Learning Representations, 2022. 1, 3, 7, 8
[57] Fei-Yue Wang, Qinghai Miao, Xuan Li, Xingxia Wang, and Yilun Lin. What does chatgpt say: The dao from algorithmic intelligence to linguistic intelligence. IEEE/CAA Journal of Automatica Sinica, 10(3):575-579, 2023. 2