{"type":"ScholarlyArticle","title":"Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning","authors":[{"name":"Guoyan Liang","slug":"guoyan-liang","given":"Guoyan","family":"Liang","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Qin Zhou","slug":"qin-zhou","given":"Qin","family":"Zhou","affiliation":"East China University of Science and Technology , Shanghai , China"},{"name":"Jingyuan Chen","slug":"jingyuan-chen","given":"Jingyuan","family":"Chen","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Zhe Wang","slug":"zhe-wang","given":"Zhe","family":"Wang","affiliation":"East China University of Science and Technology , Shanghai , China"},{"name":"Chang Yao","slug":"chang-yao","given":"Chang","family":"Yao","affiliation":"Zhejiang University , Hangzhou , China"}],"abstract":"Medical Image Segmentation (MIS) plays a crucial role in medical therapy planning and robot navigation. Prototype learning methods in MIS focus on generating segmentation masks through pixel-to-prototype comparison. However, current approaches often overlook sample diversity by using a fixed prototype per semantic class and neglect intra-class variation within each input. In this paper, we propose to generate instance-adaptive prototypes for MIS, which integrates a common prototype proposal (CPP) capturing common visual patterns and an instance-specific prototype proposal (IPP) tailored to each input. To further account for the intra-class variation, we propose to guide the IPP generation by re-weighting the intermediate feature map according to their confidence scores. These confidence scores are hierarchically generated using a transformer decoder. Additionally we introduce a novel self-supervised filtering strategy to prioritize the foreground pixels during the training of the transformer decoder. Extensive experiments demonstrate favorable performance of our method.","doi":"10.24963/ijcai.2024/117","url":"https://ijcai.org/proceedings/2024/117","pdf":"https://ijcai.org/proceedings/2024/0117.pdf","markdown":"https://ijcai.org/proceedings/2024/117.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/117","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":117,"pages":{"first":1056,"last":1064},"keywords":["Machine Learning: ML: Self-supervised Learning","Computer Vision: CV: Representation learning","Computer Vision: CV: Segmentation","Computer Vision: CV: Biomedical image analysis"],"videos":[],"references":[{"position":1,"raw":"[Abdelrahman Shaker et al., 2022] Muhammad Maaz Ab- delrahman Shaker, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, and Fahad Shahbaz Khan. Unetr++: Delving into efficient and accurate 3d medical image seg- mentation. arXiv:2212.04497, 1, 2022.","title":"Muhammad Maaz Abdelrahman Shaker","authors":"Abdelrahman Shaker","year":"2022","venue":"Unetr++: Delving into efficient and accurate 3d medical image segmentation","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Boney and Ilin, 2017] Rinu Boney and Alexander Ilin. Semi-supervised few-shot learning with prototypical net- works. CoRR abs/1711.10856, 2017.","title":"Semi-supervised few-shot learning with prototypical networks","authors":"Ilin Boney, Rinu Boney, Alexander Ilin","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cao et al., 2022] Hu Cao, Yueyue Wang, Joy Chen, Dong- sheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision, pages 205-218. Springer, 2022.","title":"Swin-unet: Unet-like pure transformer for medical image segmentation","authors":"Yueyue Hu Cao, Joy Wang, Dongsheng Chen, Xiaopeng Jiang, Qi Zhang, Manning Tian, Wang","year":"2022","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Carion et al., 2020] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020.","title":"End-to-end object detection with transformers","authors":"Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko","year":"2020","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2019] Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang. A closer look at few-shot classification. arXiv:1904.04232, 2019.","title":"A closer look at few-shot classification","authors":"Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank, Wang, Jia-Bin Huang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cheng et al., 2022] Bowen Cheng, Ishan Misra, Alexan- der G. Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 1290-1299, June 2022.","title":"Masked-attention mask transformer for universal image segmentation","authors":"Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, Rohit Girdhar","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":"[Dosovitskiy et al., 2020] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Min- derer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929, 2020.","title":"An image is worth 16x16 words: Transformers for image recognition at scale","authors":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Guerriero et al., 2018] Samantha Guerriero, Barbara Ca- puto, and Thomas Mensink. Deepncm: Deep nearest class mean classifiers. In International Conference on Learning Representations -Workshop, 2018.","title":"Deepncm: Deep nearest class mean classifiers","authors":"Samantha Guerriero, Barbara Caputo, Thomas Mensink","year":"2018","venue":"International Conference on Learning Representations -Workshop","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hatamizadeh et al., 2022] Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger R Roth, and Daguang Xu. Unetr: Trans- formers for 3d medical image segmentation. In Proceed- ings of the IEEE/CVF winter conference on applications of computer vision, pages 574-584, 2022.","title":"Unetr: Transformers for 3d medical image segmentation","authors":"Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Daguang Holger R Roth, Xu","year":"2022","venue":"Proceedings of the IEEE/CVF winter conference on applications of computer vision","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2020] Huimin Huang, Lanfen Lin, Ruofeng Tong, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xi- anhua Han, Yen-Wei Chen, and Jian Wu. Unet 3+: A full-scale connected unet for medical image segmenta- tion. In ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing, pages 1055- 1059. IEEE, 2020.","title":"Unet 3+: A full-scale connected unet for medical image segmentation","authors":"Huimin Huang, Lanfen Lin, Ruofeng Tong, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han, Yen-Wei Chen, Jian Wu","year":"2020","venue":"ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Isensee et al., 2021] Fabian Isensee, Paul F Jaeger, Si- mon AA Kohl, Jens Petersen, and Klaus H Maier-Hein. nnu-net: a self-configuring method for deep learning- based biomedical image segmentation. Nature Methods, 18:203-211, 2021.","title":"nnu-net: a self-configuring method for deep learningbased biomedical image segmentation","authors":"Fabian Isensee, Paul F Jaeger, Simon Aa Kohl, Jens Petersen, Klaus H Maier-Hein","year":"2021","venue":"Nature Methods","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Landman et al., 2015] Bennett Landman, Zhoubing Xu, J Igelsias, Martin Styner, Thomas Langerak, and Arno Klein. Miccai multi-atlas labeling beyond the cranial vault-workshop and challenge. In Proc. MICCAI Multi- Atlas Labeling Beyond Cranial Vault-Workshop Chal- lenge, volume 5, page 12, 2015.","title":"Miccai multi-atlas labeling beyond the cranial vault-workshop and challenge","authors":"Zhoubing Bennett Landman, J Xu, Martin Igelsias, Thomas Styner, Arno Langerak, Klein","year":"2015","venue":"Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault-Workshop Challenge","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2021] Shaohua Li, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong Liu, and Rick Goh. Medical image segmentation using squeeze-and-expansion transformers. arXiv:2105.09511, 2021.","title":"Medical image segmentation using squeeze-and-expansion transformers","authors":"Shaohua Li, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong Liu, Rick Goh","year":"2021","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2021/112"},{"position":14,"raw":"[Liu et al., 2021] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF in- ternational conference on computer vision, pages 10012- 10022, 2021.","title":"Swin transformer: Hierarchical vision transformer using shifted windows","authors":"Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo","year":"2021","venue":"Proceedings of the IEEE/CVF international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mensink et al., 2013] Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka. Distance-based image classification: Generalizing to new classes at near- zero cost. IEEE transactions on pattern analysis and ma- chine intelligence, 35(11):2624-2637, 2013.","title":"Distance-based image classification: Generalizing to new classes at nearzero cost","authors":"Thomas Mensink, Jakob Verbeek, Florent Perronnin, Gabriela Csurka","year":"2013","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Menze et al., 2014] Bjoern H Menze, Andras Jakab, Ste- fan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slot- boom, Roland Wiest, et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging, 34(10):1993-2024, 2014.","title":"The multimodal brain tumor image segmentation benchmark (brats)","authors":"Andras Bjoern H Menze, Stefan Jakab, Jayashree Bauer, Keyvan Kalpathy-Cramer, Justin Farahani, Yuliya Kirby, Nicole Burren, Johannes Porz, Roland Slotboom, Wiest","year":"2014","venue":"IEEE transactions on medical imaging","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Radford et al., 2021] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from nat- ural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.","title":"Learning transferable visual models from natural language supervision","authors":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark","year":"2021","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ronneberger et al., 2015] Olaf Ronneberger, Philipp Fis- cher, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention, pages 234-241. Springer, 2015.","title":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation","authors":"Ronneberger","year":"2015","venue":"Medical Image Computing and Computer-Assisted Intervention","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Santoro et al., 2016] Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lill- icrap. Meta-learning with memory-augmented neural networks. In International conference on machine learning, pages 1842-1850. PMLR, 2016.","title":"Meta-learning with memory-augmented neural networks","authors":"Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap","year":"2016","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Simpson et al., 2019] Amber L Simpson, Michela An- tonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram Van Ginneken, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv:1902.09063, 2019.","title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","authors":"Michela Amber L Simpson, Spyridon Antonelli, Michel Bakas, Keyvan Bilello, Bram Farahani, Annette Van Ginneken, Bennett A Kopp-Schneider, Geert Landman, Bjoern Litjens, Menze","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Snell et al., 2017] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. Advances in neural information processing systems, 30, 2017.","title":"Prototypical networks for few-shot learning","authors":"Jake Snell, Kevin Swersky, Richard Zemel","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Sung et al., 2018] Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. Learning to compare: Relation network for few-shot learn- ing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1199-1208, 2018.","title":"Learning to compare: Relation network for few-shot learning","authors":"Sung","year":"2018","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Tsai et al., 2003] Andy Tsai, Anthony Yezzi, William Wells, Clare Tempany, Dewey Tucker, Ayres Fan, W Eric Grimson, and Alan Willsky. A shape-based approach to the segmentation of medical imagery using level sets. IEEE transactions on medical imaging, 22(2):137-154, 2003.","title":"A shape-based approach to the segmentation of medical imagery using level sets","authors":"Andy Tsai, Anthony Yezzi, William Wells, Clare Tempany, Dewey Tucker, Ayres Fan, W Eric Grimson, Alan Willsky","year":"2003","venue":"IEEE transactions on medical imaging","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Vinyals et al., 2016] Oriol Vinyals, Charles Blundell, Timo- thy Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. Advances in neural information pro- cessing systems, 29, 2016.","title":"Matching networks for one shot learning","authors":"Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra","year":"2016","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2021a] Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. Max-deeplab: End-to-end panoptic segmentation with mask transform- ers. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, pages 5463-5474, 2021.","title":"Max-deeplab: End-to-end panoptic segmentation with mask transformers","authors":"Wang","year":"2021","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wang et al., 2021b] Wenxuan Wang, Chen Chen, Meng Ding, Hong Yu, Sen Zha, and Jiangyun Li. Transbts: Mul- timodal brain tumor segmentation using transformer. In Medical Image Computing and Computer Assisted Inter- vention, pages 109-119. Springer, 2021.","title":"Transbts: Multimodal brain tumor segmentation using transformer","authors":"Wang","year":"2021","venue":"Medical Image Computing and Computer Assisted Intervention","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wang et al., 2022] Runze Wang, Qin Zhou, and Guoyan Zheng. Few-shot medical image segmentation regular- ized with self-reference and contrastive learning. In In- ternational Conference on Medical Image Computing and Computer-Assisted Intervention, pages 514-523. Springer, 2022.","title":"Few-shot medical image segmentation regularized with self-reference and contrastive learning","authors":"Wang","year":"2022","venue":"International Conference on Medical Image Computing and Computer-Assisted Intervention","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wen et al., 2016] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A discriminative feature learning ap- proach for deep face recognition. In Computer Vision- ECCV 2016, pages 499-515. Springer, 2016.","title":"A discriminative feature learning approach for deep face recognition","authors":"Yandong Wen, Kaipeng Zhang, Zhifeng Li, Yu Qiao","year":"2016","venue":"Computer Vision-ECCV 2016","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Xiao et al., 2018] Xiao Xiao, Shen Lian, Zhiming Luo, and Shaozi Li. Weighted res-unet for high-quality retina ves- sel segmentation. In 2018 9th international conference on information technology in medicine and education, pages 327-331. IEEE, 2018.","title":"Weighted res-unet for high-quality retina vessel segmentation","authors":"Xiao Xiao, Shen Lian, Zhiming Luo, Shaozi Li","year":"2018","venue":"2018 9th international conference on information technology in medicine and education","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Yasunaga et al., 2022] Michihiro Yasunaga, Jure Leskovec, and Percy Liang. Linkbert: Pretraining language models with document links. arXiv:2203.15827, 2022.","title":"Linkbert: Pretraining language models with document links","authors":"Yasunaga","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Yu et al., 2021] Qinji Yu, Kang Dang, Nima Tajbakhsh, Demetri Terzopoulos, and Xiaowei Ding. A location- sensitive local prototype network for few-shot medical im- age segmentation. In 2021 IEEE 18th international sympo- sium on biomedical imaging (ISBI), pages 262-266. IEEE, 2021.","title":"A locationsensitive local prototype network for few-shot medical image segmentation","authors":"Yu","year":"2021","venue":"IEEE 18th international symposium on biomedical imaging (ISBI)","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Yu et al., 2022] Qihang Yu, Huiyu Wang, Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. k-means mask transformer. In Eu- ropean Conference on Computer Vision, pages 288-307. Springer, 2022.","title":"k-means mask transformer","authors":"Yu","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yuan et al., 2023] Mingze Yuan, Yingda Xia, Hexin Dong, Zifan Chen, Jiawen Yao, Mingyan Qiu, Ke Yan, Xiaoli Yin, Yu Shi, Xin Chen, et al. Devil is in the queries: Ad- vancing mask transformers for real-world medical image segmentation and out-of-distribution localization. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23879-23889, 2023.","title":"Devil is in the queries: Advancing mask transformers for real-world medical image segmentation and out-of-distribution localization","authors":"Yuan","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Zhang et al., 2021] Jianpeng Zhang, Yutong Xie, Yong Xia, and Chunhua Shen. Dodnet: Learning to segment multi- organ and tumors from multiple partially labeled datasets. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1195-1204, 2021.","title":"Dodnet: Learning to segment multiorgan and tumors from multiple partially labeled datasets","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":35,"raw":"[Zhou et al., 2018] Zongwei Zhou, Md Mahfuzur Rah- man Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Anal- ysis and Multimodal Learning for Clinical Decision Sup- port, pages 3-11. Springer, 2018.","title":"Unet++: A nested u-net architecture for medical image segmentation","authors":"Zhou","year":"2018","venue":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhou et al., 2021] Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, and Yizhou Yu. nn- former: Interleaved transformer for volumetric segmenta- tion. arXiv:2109.03201, 2021.","title":"nnformer: Interleaved transformer for volumetric segmentation","authors":"Zhou","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhou et al., 2022] Tianfei Zhou, Wenguan Wang, Ender Konukoglu, and Luc Van Gool. Rethinking semantic segmentation: A prototype view. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2582-2593, 2022.","title":"Rethinking semantic segmentation: A prototype view","authors":"Zhou","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}