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Convolutional neural networks (CNNs) have led to significant improvements in the semantic segmentation of images.
Object tracking: A survey
Alper Yilmaz, Omar Javed, and Mubarak Shah · 2006
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Estimating image segmentation difficulty
Dingding Liu, Yingen Xiong, Kari Pulli, and Linda Shapiro · 2011
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3d deeply supervised network for automated segmentation of volumetric medical images
Qi Dou, Lequan Yu, Hao Chen, Yueming Jin, Xin Yang, Jing Qin, and Pheng-Ann Heng · 2015
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Multi-atlas labeling beyond the cranial vault-workshop and challenge, 2015
Bennett Landman, Z Xu, JE Igelsias, M Styner, TR Langerak, and A Klein · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Learning deconvolution network for semantic segmentation
Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han · 2015
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Very deep convolutional networks for large-scale image recognition, 2015
Karen Simonyan and Andrew Zisserman · 2015
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Fully-convolutional siamese networks for object tracking
Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, and Philip H. S. Torr · 2016
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Optimal transport for domain adaptation
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy · 2016
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Deep reconstruction-classification networks for unsupervised domain adaptation
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li · 2016
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Multi-scale patch and multi-modality atlases for whole heart segmentation of mri
Xiahai Zhuang and Juan Shen · 2016
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Deep learning for medical image analysis
Nicholas Ayache · 2017
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Deep learning applications in medical image analysis
J. Ker, L. Wang, J. Rao, and T. Lim · 2017
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Interpretable learning for self-driving cars by visualizing causal attention
Jinkyu Kim and John Canny · 2017
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Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid · 2017
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Few-shot adversarial domain adaptation
Saeid Motiian, Quinn Jones, Seyed Iranmanesh, and Gianfranco Doretto · 2017
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Deep learning in medical image analysis
Dinggang Shen, Guorong Wu, and Heung-Il Suk · 2017
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Automatic segmentation of liver tumors from multiphase contrast-enhanced ct images based on fcns
Changjian Sun, Shuxu Guo, Huimao Zhang, Jing Li, Meimei Chen, Shuzhi Ma, Lanyi Jin, Xiaoming Liu, Xueyan Li, and Xiaohua Qian · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Curriculum domain adaptation for semantic segmentation of urban scenes
Yang Zhang, Philip David, and Boqing Gong · 2017
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Semantic-aware generative adversarial nets for unsupervised domain adaptation in chest x-ray segmentation
Cheng Chen, Qi Dou, Hao Chen, and Pheng-Ann Heng · 2018
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Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss
Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, and Pheng-Ann Heng · 2018
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End-to-end learning of driving models with surround-view cameras and route planners
Simon Hecker, Dengxin Dai, and Luc Van Gool · 2018
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Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell · 2018
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Knowledge-aided convolutional neural network for small organ segmentation
Yu Zhao, Hongwei Li, Shaohua Wan, Anjany Sekuboyina, Xiaobin Hu, Giles Tetteh, Marie Piraud, and Bjoern Menze · 2019
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Prior-aware neural network for partially-supervised multi-organ segmentation, 2019
Yuyin Zhou, Zhe Li, Song Bai, Chong Wang, Xinlei Chen, Mei Han, Elliot Fishman, and Alan Yuille · 2019
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Source-relaxed domain adaptation for image segmentation
Mathilde Bateson, Hoel Kervadec, Jose Dolz, Hervé Lombaert, and Ismail Ben Ayed · 2020
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Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation
C. Chen, Q. Dou, H. Chen, J. Qin, and P. A. Heng · 2020
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Anatomy-regularized representation learning for cross-modality medical image segmentation
Xu Chen, Chunfeng Lian, Li Wang, Hannah Deng, Tianshu Kuang, Steve Fung, Jaime Gateno, Pew-Thian Yap, James J Xia, and Dinggang Shen · 2020
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Adversarial synthesis learning enables segmentation without target modality ground truth
Yuankai Huo, Zhoubing Xu, Shunxing Bao, Albert Assad, Richard G Abramson, and Bennett A Landman · 2018
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Synseg-net: Synthetic segmentation without target modality ground truth
Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon, Shunxing Bao, Albert Assad, Tamara K. Moyo, Michael R. Savona, Richard G. Abramson, and Bennett A. Landman · 2018
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A crowdsourcing triage algorithm for geopolitical event forecasting
Mohammad Rostami, David Huber, and Tsai-Ching Lu · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Generate to adapt: Aligning domains using generative adversarial networks
Swami Sankaranarayanan, Yogesh Balaji, Carlos D Castillo, and Rama Chellappa · 2018
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Learning to adapt structured output space for semantic segmentation
Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker · 2018
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Dcan: Dual channel-wise alignment networks for unsupervised scene adaptation
Zuxuan Wu, Xintong Han, Yen-Liang Lin, Mustafa Gökhan Uzunbas, Tom Goldstein, Ser Nam Lim, and Larry S. Davis · 2018
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Gans for medical image analysis
Salome Kazeminia, Christoph Baur, Arjan Kuijper, Bram van Ginneken, Nassir Navab, Shadi Albarqouni, and Anirban Mukhopadhyay · 2020
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Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al · 2020
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Pdam: A panoptic-level feature alignment framework for unsupervised domain adaptive instance segmentation in microscopy images
D. Liu, D. Zhang, Y. Song, F. Zhang, L. O’Donnell, H. Huang, M. Chen, and W. Cai · 2020
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Umap: Uniform manifold approximation and projection for dimension reduction, 2020
Leland McInnes, John Healy, and James Melville · 2020
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Sf-uda 3d: Source-free unsupervised domain adaptation for lidar-based 3d object detection
Cristiano Saltori, Stéphane Lathuiliére, Nicu Sebe, Elisa Ricci, and Fabio Galasso · 2020
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Unsupervised domain adaptation in semantic segmentation: A review
Marco Toldo, Andrea Maracani, Umberto Michieli, and Pietro Zanuttigh · 2020
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Unpaired image-to-image translation using cycle-consistent adversarial networks, 2020
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2020
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Unsupervised domain adaptation with dual scheme fusion network for medical image segmentation
Danbing Zou, Qikui Zhu, and Pingkun Yan · 2020
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Deep symmetric adaptation network for cross-modality medical image segmentation
Xiaoting Han, Lei Qi, Qian Yu, Ziqi Zhou, Yefeng Zheng, Yinghuan Shi, and Yang Gao · 2021
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Generalize then adapt: Source-free domain adaptive semantic segmentation
Jogendra Nath Kundu, Akshay Kulkarni, Amit Singh, Varun Jampani, and R. Venkatesh Babu · 2021
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Source-free domain adaptation via avatar prototype generation and adaptation, 2021
Zhen Qiu, Yifan Zhang, Hongbin Lin, Shuaicheng Niu, Yanxia Liu, Qing Du, and Mingkui Tan · 2021
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Domain adaptation for sentiment analysis using increased intraclass separation
Mohammad Rostami and Aram Galstyan · 2021
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Unsupervised model adaptation for continual semantic segmentation
Serban Stan and Mohammad Rostami · 2021
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Self-attentive spatial adaptive normalization for cross-modality domain adaptation
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Generalized source-free domain adaptation, 2021
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2021
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Source-free domain adaptation for image segmentation
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Increasing model generalizability for unsupervised domain adaptation
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