[20] Zhang Dong, Zhang Hanwang, Tang Jinhui, Hua Xiansheng, and Sun Qianru. Causal intervention for weakly supervised semantic segmentation. In Proc. Neural Information Processing Systems (NeurIPS), 2020.
[21] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision (IJCV), 2010.
[22] Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, and Masashi Sugiyama. Provably consistent partial-label learning. Proc. Neural Information Processing Systems (NeurIPS), 33:10948-10960, 2020.
[23] S Alireza Golestaneh and Kris M Kitani. Importance of self-consistency in active learning for semantic segmentation. In Proc. British Machine Vision Conference (BMVC), 2020.
[24] Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, and William T. Freeman. Unsupervised semantic segmentation by distilling feature correspondences. In Proc. International Conference on Learning Representations (ICLR), 2022.
[25] Ruifei He, Jihan Yang, and Xiaojuan Qi. Re-distributing biased pseudo labels for semi-supervised semantic segmentation: A baseline investigation. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
[26] Tao He, Xiaoming Jin, Guiguang Ding, Lan Yi, and Chenggang Yan. Towards better uncertainty sampling: Active learning with multiple views for deep convolutional neural network. In IEEE International Conference on Multimedia and Expo (ICME), 2019.
[27] Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li. Bag of tricks for image classification with convolutional neural networks. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 558-567, 2019.
[28] Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu, and Jingdong Wang. Weakly-supervised semantic segmentation network with deep seeded region growing. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[29] Eyke Hüllermeier and Jürgen Beringer. Learning from ambiguously labeled examples. Intelligent Data Analysis (IDA), 2006.
[30] Sehyun Hwang, Sohyun Lee, Sungyeon Kim, Jungseul Ok, and Suha Kwak. Combating label distribution shift for active domain adaptation. In Proc. European Conference on Computer Vision (ECCV), pages 549-566. Springer, 2022.
[31] Suyog Dutt Jain and Kristen Grauman. Active image segmentation propagation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[32] Ajay J Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos. Multi-class active learning for image classification. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
[33] Tarun Kalluri, Girish Varma, Manmohan Chandraker, and C V Jawahar. Universal semi-supervised semantic segmentation. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), 2019.
[34] Dahyun Kang, Piotr Koniusz, Minsu Cho, and Naila Murray. Distilling self-supervised vision transformers for weakly-supervised few-shot classification & segmentation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19627-19638, 2023.
[35] Tejaswi Kasarla, Gattigorla Nagendar, Guruprasad M Hegde, Vineeth Balasubramanian, and CV Jawahar. Region-based active learning for efficient labeling in semantic segmentation. In Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2019.
[36] Rihuan Ke, Angelica Aviles-Rivero, Saurabh Pandey, Saikumar Reddy, and Carola-Bibiane Schonlieb. A three-stage self-training framework for semi-supervised semantic segmentation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[37] Zhanghan Ke, Di Qiu, Kaican Li, Qiong Yan, and Rynson W.H. Lau. Guided collaborative training for pixel-wise semi-supervised learning. In Proc. European Conference on Computer Vision (ECCV), 2020.
[38] Hoyoung Kim, Minhyeon Oh, Sehyun Hwang, Suha Kwak, and Jungseul Ok. Adaptive superpixel for active learning in semantic segmentation. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
[39] Namyup Kim, Sehyun Hwang, and Suha Kwak. Learning to detect semantic boundaries with image-level class labels. International Journal of Computer Vision (IJCV), 130(9):2131-2148, 2022.
[40] Suha Kwak, Seunghoon Hong, and Bohyung Han. Weakly supervised semantic segmentation using superpixel pooling network. In Proc. AAAI Conference on Artificial Intelligence (AAAI), 2017.
[41] Donghyeon Kwon and Suha Kwak. Semi-supervised semantic segmentation with error localization network. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[42] Xin Lai, Zhuotao Tian, Li Jiang, Shu Liu, Hengshuang Zhao, Liwei Wang, and Jiaya Jia. Semi-supervised semantic segmentation with directional context-aware consistency. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[43] Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, and Sungroh Yoon. Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.