[4] Abhijit Bendale and Terrance E Boult. Towards open set deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1563-1572, 2016. 3
[5] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021. 2, 3, 6
[6] Guangyao Chen, Peixi Peng, Xiangqian Wang, and Yonghong Tian. Adversarial reciprocal points learning for open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):8065-8081, 2021. 1, 3, 7, 8, 9
[7] Guangyao Chen, Limeng Qiao, Yemin Shi, Peixi Peng, Jia Li, Tiejun Huang, Shiliang Pu, and Yonghong Tian. Learning open set network with discriminative reciprocal points. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part III 16, pages 507-522. Springer, 2020. 3, 7
[8] Wonwoo Cho and Jaegul Choo. Towards accurate open-set recognition via background-class regularization. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXV, pages 658-674. Springer, 2022. 7
[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009. 7
[10] Akshay Raj Dhamija, Manuel Günther, and Terrance Boult. Reducing network agnostophobia. Advances in Neural Information Processing Systems, 31, 2018. 8
[11] Xuefeng Du, Xin Wang, Gabriel Gozum, and Yixuan Li. Unknown-aware object detection: Learning what you don't know from videos in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13678-13688, 2022. 2
[12] Sepideh Esmaeilpour, Bing Liu, Eric Robertson, and Lei Shu. Zero-shot out-of-distribution detection based on the pre-trained model clip. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6):6568-6576, Jun. 2022. 1, 2, 3, 7
[13] Lin Geng Foo, Hossein Rahmani, and Jun Liu. Ai-generated content (aigc) for various data modalities: A survey. arXiv preprint arXiv:2308.14177, 2023. 2
[14] Zongyuan Ge, Sergey Demyanov, Zetao Chen, and Rahul Garnavi. Generative openmax for multi-class open set classification. In British Machine Vision Conference. BMVA Press, 2017. 1, 2, 3
[15] Zongyuan Ge and Xin Wang. Evaluation of various open-set medical imaging tasks with deep neural networks. arXiv preprint arXiv:2110.10888, 2021. 1
[16] Yunrui Guo, Guglielmo Camporese, Wenjing Yang, Alessandro Sperduti, and Lamberto Ballan. Conditional variational capsule network for open set recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 103-111, 2021. 7
[17] Ziyu Guo, Yiwen Tang, Renrui Zhang, Dong Wang, Zhigang Wang, Bin Zhao, and Xuelong Li. Viewrefer: Grasp the multi-view knowledge for 3d visual grounding with gpt and prototype guidance. arXiv preprint arXiv:2303.16894, 2023. 2
[18] Mehadi Hassen and Philip K Chan. Learning a neural-network-based representation for open set recognition. In Proceedings of the 2020 SIAM International Conference on Data Mining, pages 154-162. SIAM, 2020. 1
[19] Hongzhi Huang, Yu Wang, Qinghua Hu, and Ming-Ming Cheng. Class-specific semantic reconstruction for open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 1, 3, 7
[20] Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, and Zhaopeng Tu. Is chatgpt a good translator? a preliminary study. arXiv preprint arXiv:2301.08745, 2023. 2, 3
[21] CEN Jun, Di Luan, Shiwei Zhang, Yixuan Pei, Yingya Zhang, Deli Zhao, Shaojie Shen, and Qifeng Chen. The devil is in the wrongly-classified samples: Towards unified open-set recognition. In The Eleventh International Conference on Learning Representations, 2022. 3
[22] Nazmul Karim, Niluthpol Chowdhury Mithun, Abhinav Rajvanshi, Han-pang Chiu, Supun Samarasekera, and Nazanin Rahnavard. C-sfda: A curriculum learning aided self-training framework for efficient source free domain adaptation. arXiv preprint arXiv:2303.17132, 2023. 2