[21] Kai Katsumata, Duc Minh Vo, and Hideki Nakayama. Ossgan: Open-set semi-supervised image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
[22] Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, and Gui-Song Xia. Expanding low-density latent regions for open-set object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9591-9600, 2022.
[23] Colin Troisemaine, Vincent Lemaire, Stéphane Gosselin, Alexandre Reiffers-Masson, Joachim Flocon-Cholet, and Sandrine Vaton. Novel class discovery: an introduction and key concepts. arXiv preprint arXiv:2302.12028, 2023.
[24] Yuyang Zhao, Zhun Zhong, Nicu Sebe, and Gim Hee Lee. Novel class discovery in semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
[25] Muli Yang, Yuehua Zhu, Jiaping Yu, Among Wu, and Cheng Deng. Divide and conquer: Compositional experts for generalized novel class discovery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14268-14277, 2022.
[26] KJ Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, and Vineeth N Balasubramanian. Novel class discovery without forgetting. In European Conference on Computer Vision, pages 570-586. Springer, 2022.
[27] Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman. Generalized category discovery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
[28] Sheng Zhang, Salman Khan, Zhiqiang Shen, Muzammal Naseer, Guangyi Chen, and Fahad Khan. Promptal: Contrastive affinity learning via auxiliary prompts for generalized novel category discovery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
[29] Nan Pu, Zhun Zhong, and Nicu Sebe. Dynamic conceptionsal contrastive learning for generalized category discovery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
[30] Florent Chiaroni, Jose Dolz, Ziko Imtiaz Masud, Amar Mitiche, and Ismail Ben Ayed. Parametric information maximization for generalized category discovery. arXiv preprint arXiv:2212.00334, 2022.
[31] Xin Wen, Bingchen Zhao, and Xiaojuan Qi. Parametric classification for generalized category discovery: A baseline study. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16590-16600, 2023.
[32] Yixin Fei, Zhongkai Zhao, Siwei Yang, and Bingchen Zhao. Xcon: Learning with experts for fine-grained category discovery. In British Machine Vision Conference, 2022.
[33] Wenbin An, Feng Tian, Ping Chen, Siliang Tang, Qinghua Zheng, and Qian Ying Wang. Fine-grained category discovery under coarse-grained supervision with hierarchical weighted self-contrastive learning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2022.
[34] Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng. Deep long-tailed learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
[35] Yingjun Du, Jiayi Shen, Xiantong Zhen, and Cees GM Snoek. Superdisco: Super-class discovery improves visual recognition for the long-tail. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
[36] Yingxiao Du and Jianxin Wu. No one left behind: Improving the worst categories in long-tailed learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
[37] Sumyeong Ahn, Jongwoo Ko, and Se-Young Yun. Cuda: Curriculum of data augmentation for long-tailed recognition. In Proceedings of the International Conference on Learning Representations, 2023.
[38] Haley A Vlach. How we categorize objects is related to how we remember them: The shape bias as a memory bias. Journal of Experimental Child Psychology, 152:12-30, 2016.
[39] Thomas J Palmeri and Isabel Gauthier. Visual object understanding. Nature Reviews Neuroscience, 5(4): 291-303, 2004.
[40] Alec Scharff, John Palmer, and Cathleen M Moore. Evidence of fixed capacity in visual object categorization. Psychonomic bulletin & review, 18:713-721, 2011.
[41] Sandra Reinert, Mark Hübener, Tobias Bonhoeffer, and Pieter M Goltstein. Mouse prefrontal cortex represents learned rules for categorization. Nature, 593(7859):411-417, 2021.
[42] Pieter M Goltstein, Sandra Reinert, Tobias Bonhoeffer, and Mark Hubener. Mouse visual cortex areas represent perceptual and semantic features of learned visual categories. Nature Neuroscience, 24(10): 1441-1451, 2021.