{"type":"ScholarlyArticle","title":"ICDA: Illumination-Coupled Domain Adaptation Framework for Unsupervised Nighttime Semantic Segmentation","authors":[{"name":"Chenghao Dong","slug":"chenghao-dong","given":"Chenghao","family":"Dong","affiliation":"School of Computer Science (National Pilot Software Engineering School) , Beijing University of Posts and Telecommunications"},{"name":"Xuejing Kang","slug":"xuejing-kang","given":"Xuejing","family":"Kang","affiliation":"School of Computer Science (National Pilot Software Engineering School) , Beijing University of Posts and Telecommunications"},{"name":"Anlong Ming","slug":"anlong-ming","given":"Anlong","family":"Ming","affiliation":"School of Computer Science (National Pilot Software Engineering School) , Beijing University of Posts and Telecommunications"}],"abstract":"The performance of nighttime semantic segmentation has been significantly improved thanks to recent unsupervised methods. However, these methods still suffer from complex domain gaps, i.e., the challenging illumination gap and the inherent dataset gap. In this paper, we propose the illumination-coupled domain adaptation framework(ICDA) to effectively avoid the illumination gap and mitigate the dataset gap by coupling daytime and nighttime images as a whole with semantic relevance. Specifically, we first design a new composite enhancement method(CEM) that considers not only illumination but also spatial consistency to construct the source and target domain pairs, which provides the basic adaptation unit for our ICDA. Next, to avoid the illumination gap, we devise the Deformable Attention Relevance(DAR) module to capture the semantic relevance inside each domain pair, which can couple the daytime and nighttime images at the feature level and adaptively guide the predictions of nighttime images. Besides, to mitigate the dataset gap and acquire domain-invariant semantic relevance, we propose the Prototype-based Class Alignment(PCA) module, which improves the usage of category information and performs fine-grained alignment. Extensive experiments show that our method reduces the complex domain gaps and achieves state-of-the-art performance for nighttime semantic segmentation.  Our code is available at https://github.com/chenghaoDong666/ICDA.","doi":"10.24963/ijcai.2023/75","url":"https://ijcai.org/proceedings/2023/75","pdf":"https://ijcai.org/proceedings/2023/0075.pdf","markdown":"https://ijcai.org/proceedings/2023/75.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/75","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":75,"pages":{"first":672,"last":680},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Bruggemann et al., 2023] David Bruggemann, Christos Sakaridis, Prune Truong, and Luc Van Gool. Refign: Align and refine for adaptation of semantic segmentation to adverse conditions. In WACV, 2023.","title":"Refign: Align and refine for adaptation of semantic segmentation to adverse conditions","authors":"David Bruggemann, Christos Sakaridis, Prune Truong, Luc Van Gool","year":"2023","venue":"WACV","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2017] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convo- lutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intel- ligence, 40(4):834-848, 2017.","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","authors":"Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L Yuille","year":"2017","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cordts et al., 2016] Marius Cordts, Mohamed Omran, Se- bastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene under- standing. In Proceedings of the IEEE conference on com- puter vision and pattern recognition, pages 3213-3223, 2016.","title":"The cityscapes dataset for semantic urban scene understanding","authors":"Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dai and Van Gool, 2018] Dengxin Dai and Luc Van Gool. Dark model adaptation: Semantic image segmentation from daytime to nighttime. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 3819-3824. IEEE, 2018.","title":"Dark model adaptation: Semantic image segmentation from daytime to nighttime","authors":"Van Dai, ; Gool, Dengxin Dai, Luc Van Gool","year":"2018","venue":"21st International Conference on Intelligent Transportation Systems (ITSC)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dai et al., 2017] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. De- formable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017.","title":"Deformable convolutional networks","authors":"Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, Yichen Wei","year":"2017","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Gao et al., 2022] Huan Gao, Jichang Guo, Guoli Wang, and Qian Zhang. Cross-domain correlation distillation for un- supervised domain adaptation in nighttime semantic seg- mentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9913- 9923, 2022.","title":"Cross-domain correlation distillation for unsupervised domain adaptation in nighttime semantic segmentation","authors":"Huan Gao, Jichang Guo, Guoli Wang, Qian Zhang","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Geiger et al., 2012] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, pages 3354- 3361. IEEE, 2012.","title":"Are we ready for autonomous driving? the kitti vision benchmark suite","authors":"Andreas Geiger, Philip Lenz, Raquel Urtasun","year":"2012","venue":"IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hoffman et al., 2018] Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell. Cycada: Cycle-consistent ad- versarial domain adaptation. In International conference on machine learning, pages 1989-1998. Pmlr, 2018.","title":"Cycada: Cycle-consistent adversarial domain adaptation","authors":"Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, Trevor Darrell","year":"2018","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hoyer et al., 2022] Lukas Hoyer, Dengxin Dai, and Luc Van Gool. Daformer: Improving network architectures and training strategies for domain-adaptive semantic seg- mentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9924- 9935, 2022.","title":"Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation","authors":"Lukas Hoyer, Dengxin Dai, Luc Van Gool","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Huang et al., 2020] Jiaxing Huang, Shijian Lu, Dayan Guan, and Xiaobing Zhang. Contextual-relation consis- tent domain adaptation for semantic segmentation. In Eu- ropean conference on computer vision, pages 705-722. Springer, 2020.","title":"Contextual-relation consistent domain adaptation for semantic segmentation","authors":"Jiaxing Huang, Shijian Lu, Dayan Guan, Xiaobing Zhang","year":"2020","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jiang et al., 2022] Zhengkai Jiang, Yuxi Li, Ceyuan Yang, Peng Gao, Yabiao Wang, Ying Tai, and Chengjie Wang. Prototypical contrast adaptation for domain adaptive se- mantic segmentation. arXiv preprint arXiv:2207.06654, 2022.","title":"Prototypical contrast adaptation for domain adaptive semantic segmentation","authors":"Zhengkai Jiang, Yuxi Li, Ceyuan Yang, Peng Gao, Yabiao Wang, Ying Tai, Chengjie Wang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2019] Yunsheng Li, Lu Yuan, and Nuno Vascon- celos. Bidirectional learning for domain adaptation of se- mantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6936-6945, 2019.","title":"Bidirectional learning for domain adaptation of semantic segmentation","authors":"Yunsheng Li, Lu Yuan, Nuno Vasconcelos","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Lin et al., 2017] Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid. Refinenet: Multi-path refinement net- works for high-resolution semantic segmentation. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, pages 1925-1934, 2017.","title":"Refinenet: Multi-path refinement networks for high-resolution semantic segmentation","authors":"Guosheng Lin, Anton Milan, Chunhua Shen, Ian Reid","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Loshchilov and Hutter, 2019] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In Inter- national Conference on Learning Representations, 2019.","title":"Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization","authors":"Hutter Loshchilov","year":"2019","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Luo et al., 2019] Yawei Luo, Liang Zheng, Tao Guan, Jun- qing Yu, and Yi Yang. Taking a closer look at domain shift: Category-level adversaries for semantics consistent do- main adaptation. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 2507-2516, 2019.","title":"Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation","authors":"Yawei Luo, Liang Zheng, Tao Guan, Junqing Yu, Yi Yang","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Ma et al., 2021] Haoyu Ma, Xiangru Lin, Zifeng Wu, and Yizhou Yu. Coarse-to-fine domain adaptive semantic seg- mentation with photometric alignment and category-center regularization. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 4051-4060, 2021.","title":"Coarse-to-fine domain adaptive semantic segmentation with photometric alignment and category-center regularization","authors":"Haoyu Ma, Xiangru Lin, Zifeng Wu, Yizhou Yu","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Pan et al., 2020] Fei Pan, Inkyu Shin, Francois Rameau, Seokju Lee, and In So Kweon. Unsupervised intra- domain adaptation for semantic segmentation through self-supervision. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 3764-3773, 2020.","title":"Unsupervised intradomain adaptation for semantic segmentation through self-supervision","authors":"Fei Pan, Inkyu Shin, Francois Rameau, Seokju Lee, In So Kweon","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Rocco et al., 2017] Ignacio Rocco, Relja Arandjelovic, and Josef Sivic. Convolutional neural network architecture for geometric matching. In Proceedings of the IEEE confer- ence on computer vision and pattern recognition, pages 6148-6157, 2017.","title":"Convolutional neural network architecture for geometric matching","authors":"Ignacio Rocco, Relja Arandjelovic, Josef Sivic","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Sakaridis et al., 2019] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime im- age segmentation. In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pages 7374- 7383, 2019.","title":"Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation","authors":"Christos Sakaridis, Dengxin Dai, Luc Van Gool","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Sakaridis et al., 2020] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, 2020.","title":"Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation","authors":"Christos Sakaridis, Dengxin Dai, Luc Van Gool","year":"2020","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tarvainen and Valpola, 2017] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results. Advances in neural information pro- cessing systems, 30, 2017.","title":"Mean teachers are better role models: Weightaveraged consistency targets improve semi-supervised deep learning results","authors":"Valpola Tarvainen, Antti Tarvainen, Harri Valpola","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Toldo et al., 2020] Marco Toldo, Andrea Maracani, Um- berto Michieli, and Pietro Zanuttigh. Unsupervised do- main adaptation in semantic segmentation: a review. Tech- nologies, 8(2):35, 2020.","title":"Unsupervised domain adaptation in semantic segmentation: a review","authors":"Marco Toldo, Andrea Maracani, Umberto Michieli, Pietro Zanuttigh","year":"2020","venue":"Technologies","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Toldo et al., 2021] Marco Toldo, Umberto Michieli, and Pietro Zanuttigh. Unsupervised domain adaptation in se- mantic segmentation via orthogonal and clustered embed- dings. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1358-1368, 2021.","title":"Unsupervised domain adaptation in semantic segmentation via orthogonal and clustered embeddings","authors":"Marco Toldo, Umberto Michieli, Pietro Zanuttigh","year":"2021","venue":"Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Truong et al., 2021] Prune Truong, Martin Danelljan, Fisher Yu, and Luc Van Gool. Warp consistency for unsupervised learning of dense correspondences. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10346-10356, 2021.","title":"Warp consistency for unsupervised learning of dense correspondences","authors":"Truong","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Tsai et al., 2018] Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, and Manmo- han Chandraker. Learning to adapt structured output space for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7472-7481, 2018.","title":"Learning to adapt structured output space for semantic segmentation","authors":"Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, Manmohan Chandraker","year":"2018","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Vu et al., 2019] Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez. Advent: Ad- versarial entropy minimization for domain adaptation in semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2517-2526, 2019.","title":"Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation","authors":"Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, Patrick Pérez","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2020] Haoran Wang, Tong Shen, Wei Zhang, Ling-Yu Duan, and Tao Mei. Classes matter: A fine- grained adversarial approach to cross-domain semantic segmentation. In European conference on computer vi- sion, pages 642-659. Springer, 2020.","title":"Classes matter: A finegrained adversarial approach to cross-domain semantic segmentation","authors":"Haoran Wang, Tong Shen, Wei Zhang, Ling-Yu Duan, Tao Mei","year":"2020","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2021] Qin Wang, Dengxin Dai, Lukas Hoyer, Luc Van Gool, and Olga Fink. Domain adaptive seman- tic segmentation with self-supervised depth estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8515-8525, 2021.","title":"Domain adaptive semantic segmentation with self-supervised depth estimation","authors":"Qin Wang, Dengxin Dai, Lukas Hoyer, Luc Van Gool, Olga Fink","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wu et al., 2021a] Xinyi Wu, Zhenyao Wu, Hao Guo, Lili Ju, and Song Wang. Dannet: A one-stage domain adapta- tion network for unsupervised nighttime semantic segmen- tation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15769- 15778, 2021.","title":"Dannet: A one-stage domain adapta-tion network for unsupervised nighttime semantic segmentation","authors":"Wu","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wu et al., 2021b] Xinyi Wu, Zhenyao Wu, Lili Ju, and Song Wang. A one-stage domain adaptation network with image alignment for unsupervised nighttime semantic segmenta- tion. IEEE Transactions on Pattern Analysis & Machine Intelligence, (01):1-1, 2021.","title":"A one-stage domain adaptation network with image alignment for unsupervised nighttime semantic segmentation","authors":"Wu","year":"2021","venue":"IEEE Transactions on Pattern Analysis & Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Xia et al., 2022] Zhuofan Xia, Xuran Pan, Shiji Song, Li Er- ran Li, and Gao Huang. Vision transformer with de- formable attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4794-4803, 2022.","title":"Vision transformer with deformable attention","authors":"Xia","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xie et al., 2021] Enze Xie, Wenhai Wang, Zhiding Yu, An- ima Anandkumar, Jose M Alvarez, and Ping Luo. Seg- former: Simple and efficient design for semantic segmen- tation with transformers. Advances in Neural Information Processing Systems, 34:12077-12090, 2021.","title":"Segformer: Simple and efficient design for semantic segmentation with transformers","authors":"Xie","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xie et al., 2022] Binhui Xie, Shuang Li, Mingjia Li, Chi Harold Liu, Gao Huang, and Guoren Wang. Sepico: Semantic-guided pixel contrast for domain adaptive se- mantic segmentation. arXiv preprint arXiv:2204.08808, 2022.","title":"Sepico: Semantic-guided pixel contrast for domain adaptive semantic segmentation","authors":"Binhui Xie, Shuang Li, Mingjia Li, Chi Harold Liu, Gao Huang, Guoren Wang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xu et al., 2021] Qi Xu, Yinan Ma, Jing Wu, Chengnian Long, and Xiaolin Huang. Cdada: A curriculum domain adaptation for nighttime semantic segmentation. In Pro- ceedings of the IEEE/CVF International Conference on Computer Vision, pages 2962-2971, 2021.","title":"Cdada: A curriculum domain adaptation for nighttime semantic segmentation","authors":"Qi Xu, Yinan Ma, Jing Wu, Chengnian Long, Xiaolin Huang","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yang and Soatto, 2020] Yanchao Yang and Stefano Soatto. Fda: Fourier domain adaptation for semantic segmenta- tion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4085- 4095, 2020.","title":"Fda: Fourier domain adaptation for semantic segmentation","authors":"Yang, Soatto ;, Yanchao Yang, Stefano Soatto","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Yu et al., 2020] Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell. Bdd100k: A diverse driv- ing dataset for heterogeneous multitask learning. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2636-2645, 2020.","title":"Bdd100k: A diverse driving dataset for heterogeneous multitask learning","authors":"Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, Trevor Darrell","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhang et al., 2021] Pan Zhang, Bo Zhang, Ting Zhang, Dong Chen, Yong Wang, and Fang Wen. Prototypical pseudo label denoising and target structure learning for do- main adaptive semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12414-12424, 2021.","title":"Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation","authors":"Zhang","year":"2021","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhao et al., 2017] Hengshuang Zhao, Jianping Shi, Xiao- juan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881- 2890, 2017.","title":"Pyramid scene parsing network","authors":"Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhu et al., 2017] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceed- ings of the IEEE international conference on computer vi- sion, pages 2223-2232, 2017.","title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","authors":"Zhu","year":"2017","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}