[21] Alexander Kolesnikov and Christoph H Lampert. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In European conference on computer vision, pages 695-711. Springer, 2016. 1, 3
[22] Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu, and Jingdong Wang. Weakly-supervised semantic segmentation network with deep seeded region growing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7014-7023, 2018. 1, 3
[23] Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, and Thomas S Huang. Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7268-7277, 2018. 1, 3
[24] Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, and Sungroh Yoon. Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5267-5276, 2019. 1, 3
[25] Wataru Shimoda and Keiji Yanai. Self-supervised difference detection for weakly-supervised semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5208-5217, 2019. 1, 3
[26] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12275–12284, 2020. 1, 3
[27] Yu-Ting Chang, Qiaosong Wang, Wei-Chih Hung, Robinson Piramuthu, Yi-Hsuan Tsai, and Ming-Hsuan Yang. Weakly-supervised semantic segmentation via sub-category exploration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8991-9000, 2020. 1, 3
[28] Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Yun Fu. Tell me where to look: Guided attention inference network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9215-9223, 2018. 1, 3
[29] Qibin Hou, PengTao Jiang, Yunchao Wei, and Ming-Ming Cheng. Self-erasing network for integral object attention. Advances in Neural Information Processing Systems, 31, 2018. 1, 3
[30] Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, and Linlin Shen. C2am: Contrastive learning of class-agnostic activation map for weakly supervised object localization and semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 989–998, 2022. 2, 3, 4, 5, 6, 7
[31] Yusuf Aytar, Carl Vondrick, and Antonio Torralba. Soundnet: Learning sound representations from unlabeled video. In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), 2016. 2
[32] Andrew Owens, Jiajun Wu, Josh H. McDermott, William T. Freeman, and Antonio Torralba. Ambient sound provides supervision for visual learning. In Proceedings of the European Conference on Computer Vision (ECCV), pages 801-816, 2016. 2
[33] Relja Arandjelovic and Andrew Zisserman. Look, listen and learn. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 609-617, 2017. 2
[34] Bruno Korbar, Du Tran, and Lorenzo Torresani. Cooperative learning of audio and video models from self-supervised synchronization. In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), 2018. 2
[35] Hang Zhao, Chuang Gan, Andrew Rouditchenko, Carl Vondrick, Josh McDermott, and Antonio Torralba. The sound of pixels. In Proceedings of the European Conference on Computer Vision (ECCV), pages 570-586, 2018. 2