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@incollection{alex_net,
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pages = {1097--1105},
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}

@inproceedings{deeplab_v1,
  author    = {Liang{-}Chieh Chen and
               George Papandreou and
               Iasonas Kokkinos and
               Kevin Murphy and
               Alan L. Yuille},
  editor    = {Yoshua Bengio and
               Yann LeCun},
  title     = {Semantic Image Segmentation with Deep Convolutional Nets and Fully
               Connected CRFs},
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@inproceedings{rfh_objdet,
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title = {Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation},
year = {2014},
isbn = {9781479951185},
publisher = {IEEE Computer Society},
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numpages = {8},
series = {CVPR ’14}
}

@incollection{faster_rcnn,
title = {Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},
author = {Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
booktitle = {Advances in Neural Information Processing Systems 28},
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pages = {91--99},
year = {2015},
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}

@inproceedings{hr_net,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
  booktitle={CVPR},
  year={2019}
}

@article{hr_net_pami,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and 
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and 
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal   = {TPAMI},
  year={2019}
}

@InProceedings{nvidia_cvpr19,
author = {Zhu, Yi and Sapra, Karan and Reda, Fitsum A. and Shih, Kevin J. and Newsam, Shawn and Tao, Andrew and Catanzaro, Bryan},
title = {Improving Semantic Segmentation via Video Propagation and Label Relaxation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@article{efficientps,
title={EfficientPS: Efficient Panoptic Segmentation},
author={Mohan, Rohit and Valada, Abhinav},
journal={arXiv preprint arXiv:2004.02307},
year={2020}
}

@inproceedings{sota_imclass,
       author = {Touvron, Hugo and Vedaldi, Andrea and Douze, Matthijs and J{\'e}gou, Herv{\'e}},
       title = {Fixing the train-test resolution discrepancy},
       booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
       year = {2019},
}

@article{sota_objdet,
title={ResNeSt: Split-Attention Networks},
author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander},
journal={arXiv preprint arXiv:2004.08955},
year={2020}
}

@article{sota_objtrack,
title={Fast Visual Object Tracking with Rotated Bounding Boxes},
author={Chen, Bao Xin and Tsotsos, John K},
journal={arXiv preprint arXiv:1907.03892},
year={2019}
}

@article{fast_track,
title={Fast Online Object Tracking and Segmentation: A Unifying Approach},
author={Wang, Qiang and Zhang, Li and Bertinetto, Luca and Hu, Weiming and Torr, Philip HS},
journal={arXiv preprint arXiv:1812.05050},
year={2018}
}

@article{siamrpn,
title={SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks},
author={Li, Bo and Wu, Wei and Wang, Qiang and Zhang, Fangyi and Xing, Junliang and Yan, Junjie},
journal={arXiv preprint arXiv:1812.11703},
year={2018}
}

@InProceedings{semi_sup_seg_1,
author = {Zhou, Yi and He, Xiaodong and Huang, Lei and Liu, Li and Zhu, Fan and Cui, Shanshan and Shao, Ling},
title = {Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
@InProceedings{lp_class,
author = {Iscen, Ahmet and Tolias, Giorgos and Avrithis, Yannis and Chum, Ondrej},
title = {Label Propagation for Deep Semi-Supervised Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@InProceedings{semi_sup_seg_2,
author = {Lee, Jungbeom and Kim, Eunji and Lee, Sungmin and Lee, Jangho and Yoon, Sungroh},
title = {FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using Stochastic Inference},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@InProceedings{temporal_cycle_1,
author = {Dwibedi, Debidatta and Aytar, Yusuf and Tompson, Jonathan and Sermanet, Pierre and Zisserman, Andrew},
title = {Temporal Cycle-Consistency Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
@InProceedings{vid_obj_seg_lp,
author = {Oh, Seoung Wug and Lee, Joon-Young and Xu, Ning and Kim, Seon Joo},
title = {Fast User-Guided Video Object Segmentation by Interaction-And-Propagation Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
@InProceedings{domain_seg_1,
author = {Vu, Tuan-Hung and Jain, Himalaya and Bucher, Maxime and Cord, Matthieu and Perez, Patrick},
title = {ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@InProceedings{domain_seg_2,
author = {Li, Yunsheng and Yuan, Lu and Vasconcelos, Nuno},
title = {Bidirectional Learning for Domain Adaptation of Semantic Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@InProceedings{domain_seg_3,
author = {Chen, Yun-Chun and Lin, Yen-Yu and Yang, Ming-Hsuan and Huang, Jia-Bin},
title = {CrDoCo: Pixel-Level Domain Transfer With Cross-Domain Consistency},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}


@incollection{domain_seg_nips_2,
title = {Multi-source Domain Adaptation for Semantic Segmentation},
author = {Zhao, Sicheng and Li, Bo and Yue, Xiangyu and Gu, Yang and Xu, Pengfei and Hu, Runbo and Chai, Hua and Keutzer, Kurt},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {7287--7300},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8949-multi-source-domain-adaptation-for-semantic-segmentation.pdf}
}

@incollection{semi_aug_1,
title = {MixMatch: A Holistic Approach to Semi-Supervised Learning},
author = {Berthelot, David and Carlini, Nicholas and Goodfellow, Ian and Papernot, Nicolas and Oliver, Avital and Raffel, Colin A},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {5049--5059},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8749-mixmatch-a-holistic-approach-to-semi-supervised-learning.pdf}
}

@incollection{semi_aug_2,
title = {Consistency-based Semi-supervised Learning for Object detection},
author = {Jeong, Jisoo and Lee, Seungeui and Kim, Jeesoo and Kwak, Nojun},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {10759--10768},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/9259-consistency-based-semi-supervised-learning-for-object-detection.pdf}
}

@InProceedings{self_sup_iccv,
author = {Larsson, Mans and Stenborg, Erik and Toft, Carl and Hammarstrand, Lars and Sattler, Torsten and Kahl, Fredrik},
title = {Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}

@inproceedings{self_sup_aaai,
 author = {Zhan, Xiaohang and Liu, Ziwei and Luo, Ping and Tang, Xiaoou and Loy, Chen Change},
 title = {Mix-and-Match Tuning for Self-Supervised Semantic Segmentation},
 booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
 month = {February},
 year = {2018} 
}

@inproceedings{cs_dataset,
title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2016}
}
@article{as_dataset,
  title={The apolloscape open dataset for autonomous driving and its application},
  author={Wang, Peng and Huang, Xinyu and Cheng, Xinjing and Zhou, Dingfu and Geng, Qichuan and Yang, Ruigang},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2019},
  publisher={IEEE}
}

@INPROCEEDINGS {argo_dataset,
  author = {Ming-Fang Chang and John W Lambert and Patsorn Sangkloy and Jagjeet Singh
       and Slawomir Bak and Andrew Hartnett and De Wang and Peter Carr
       and Simon Lucey and Deva Ramanan and James Hays},
  title = {Argoverse: 3D Tracking and Forecasting with Rich Maps},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019}
}

@inproceedings{taskonomy2018,
 title={Taskonomy: Disentangling Task Transfer Learning},
 author={Amir R. Zamir and Alexander Sax and William B. Shen and Leonidas J. Guibas and Jitendra Malik and Silvio Savarese},
 booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year={2018},
 organization={IEEE},
}

@misc{compete_1,
    title={Improving Semantic Segmentation via Self-Training},
    author={Yi Zhu and Zhongyue Zhang and Chongruo Wu and Zhi Zhang and Tong He and Hang Zhang and R. Manmatha and Mu Li and Alexander Smola},
    year={2020},
    eprint={2004.14960},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
@misc{lazy_label,
    title={A multi-task U-net for segmentation with lazy labels},
    author={Rihuan Ke and Aurélie Bugeau and Nicolas Papadakis and Peter Schuetz and Carola-Bibiane Schönlieb},
    year={2019},
    eprint={1906.12177},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@INPROCEEDINGS{lp_2010,
  author={V. {Badrinarayanan} and F. {Galasso} and R. {Cipolla}},
  booktitle={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition}, 
  title={Label propagation in video sequences}, 
  year={2010},
  volume={},
  number={},
  pages={3265-3272},}
  
  @InProceedings{lp_iccvw,
               author = {Budvytis, I. and Sauer, P. and Roddick, T. and Breen, K. and Cipolla, R.},
               title = {Large Scale Labelled Video Data Augmentation for Semantic Segmentation in Driving Scenarios},
               booktitle = {5th Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving in IEEE International Conference on Computer Vision (ICCV)},
               month = {October},
               year = {2017}
}

@article{lp_2013,
               author    = {Vijay Badrinarayanan and Ignas Budvytis and Roberto Cipolla},
               title     = {Semi-Supervised Video Segmentation Using Tree Structured Graphical Models},
               journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
               volume    = {35},
               number    = {11},
               pages     = {2751--2764},
               year      = {2013}  
}

@inproceedings{lp_eccv,
title = "Can ground truth label propagation from video help semantic segmentation?",
year = "2016",
month = "1",
day = "1",
doi = "10.1007/978-3-319-49409-8_66",
language = "English",
isbn = "9783319494081",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "804--820",
editor = "Gang Hua and Herve Jegou",
booktitle = "Computer Vision – ECCV 2016 Workshops, Proceedings",
}

@inproceedings{lp_2006,
  added-at = {2006-06-16T10:34:37.000+0200},
  author = {Zhu, Xiaojin and Ghahramani, Zoubin},
  biburl = {https://www.bibsonomy.org/bibtex/25ddd451b8facd00d9493358a3db9b733/ldietz},
  citeulike-article-id = {310457},
  interhash = {b45f08fc36ee3a972a246ac1716aab15},
  intrahash = {5ddd451b8facd00d9493358a3db9b733},
  keywords = {clustering contraint},
  priority = {4},
  timestamp = {2006-06-16T10:34:37.000+0200},
  title = {Learning from Labeled and Unlabeled Data with
Label Propagation},
  url = {http://www.scholar.google.com/url?sa=U\&q=http://www.cs.cmu.edu/~zhuxj/pub/propagate.ps.gz},
  year = 2002
}

@incollection{gal_main,
title = {What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?},
author = {Kendall, Alex and Gal, Yarin},
booktitle = {Advances in Neural Information Processing Systems 30},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {5574--5584},
year = {2017},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7141-what-uncertainties-do-we-need-in-bayesian-deep-learning-for-computer-vision.pdf}
}
@incollection{uncer_label_1,
title = {Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels},
author = {Neverova, Natalia and Novotny, David and Vedaldi, Andrea},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {920--928},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8378-correlated-uncertainty-for-learning-dense-correspondences-from-noisy-labels.pdf}
}
@inproceedings{uncer_label_2,
  author    = {Sungjoon Choi and
               Kyungjae Lee and
               Sungbin Lim and
               Songhwai Oh},
  title     = {Uncertainty-Aware Learning from Demonstration Using Mixture Density
               Networks with Sampling-Free Variance Modeling},
  booktitle = {2018 {IEEE} International Conference on Robotics and Automation, {ICRA}
               2018, Brisbane, Australia, May 21-25, 2018},
  pages     = {6915--6922},
  publisher = {{IEEE}},
  year      = {2018},
  url       = {https://doi.org/10.1109/ICRA.2018.8462978},
  doi       = {10.1109/ICRA.2018.8462978},
  timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
  biburl    = {https://dblp.org/rec/conf/icra/ChoiLLO18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@incollection{uncer_nips_2,
title = {Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections},
author = {Yehezkel Rohekar, Raanan and Gurwicz, Yaniv and Nisimov, Shami and Novik, Gal},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {4244--4254},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8677-modeling-uncertainty-by-learning-a-hierarchy-of-deep-neural-connections.pdf}
}

@incollection{uncer_nips_3,
title = {On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks},
author = {Thulasidasan, Sunil and Chennupati, Gopinath and Bilmes, Jeff A and Bhattacharya, Tanmoy and Michalak, Sarah},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {13888--13899},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/9540-on-mixup-training-improved-calibration-and-predictive-uncertainty-for-deep-neural-networks.pdf}
}
@INPROCEEDINGS{fcn_cvpr,
  author={J. {Long} and E. {Shelhamer} and T. {Darrell}},
  booktitle={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Fully convolutional networks for semantic segmentation}, 
  year={2015},
  volume={},
  number={},
  pages={3431-3440},}
  
  
@misc{semi_sup_depth,
title={Semi-supervised semantic segmentation needs strong, high-dimensional perturbations},
author={Geoff French and Timo Aila and Samuli Laine and Michal Mackiewicz and Graham Finlayson},
year={2020},
url={https://openreview.net/forum?id=B1eBoJStwr}
}
@inproceedings{semi_sup_depth,
title={Semantically-Guided Representation Learning for Self-Supervised Monocular Depth},
author={Vitor Guizilini and Rui Hou and Jie Li and Rares Ambrus and Adrien Gaidon},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=ByxT7TNFvH}
}

@InProceedings{multi_domain_seg,
   title = {Multi-source Domain Adaptation for Semantic Segmentation},
   author = {Zhao, Sicheng and Li, Bo and Yue, Xiangyu and Gu, Yang and Xu, Pengfei and Tan, Hu, Runbo and Chai, Hua and   Keutzer, Kurt},
   booktitle = {Advances in Neural Information Processing Systems},
   year = {2019}
}

@InProceedings{domain_seg_1,
author = {Zhang, Yang and David, Philip and Gong, Boqing},
title = {Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}

@InProceedings{domain_seg_2,
author = {Zou, Yang and Yu, Zhiding and Vijaya Kumar, B.V.K. and Wang, Jinsong},
title = {Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}

@article{domain_seg_3,
  title={BUDA: Boundless Unsupervised Domain Adaptation in Semantic Segmentation},
  author={Bucher, Maxime and Vu, Tuan-Hung and Cord, Mathieu and P{\'e}rez, Patrick},
  booktitle={Arxiv},
  year={2020}
}
@InProceedings{non_deep_sem_seg,
author="Carreira, Jo{\~a}o
and Caseiro, Rui
and Batista, Jorge
and Sminchisescu, Cristian",
editor="Fitzgibbon, Andrew
and Lazebnik, Svetlana
and Perona, Pietro
and Sato, Yoichi
and Schmid, Cordelia",
title="Semantic Segmentation with Second-Order Pooling",
booktitle="Computer Vision -- ECCV 2012",
year="2012",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="430--443",
}

@INPROCEEDINGS{rcnn_1,
  author={R. {Girshick} and J. {Donahue} and T. {Darrell} and J. {Malik}},
  booktitle={2014 IEEE Conference on Computer Vision and Pattern Recognition}, 
  title={Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation}, 
  year={2014},
  volume={},
  number={},
  pages={580-587},}
  
  @INPROCEEDINGS{non_deep_sem_seg_2,
  author={S. {Fidler} and R. {Mottaghi} and A. {Yuille} and R. {Urtasun}},
  booktitle={2013 IEEE Conference on Computer Vision and Pattern Recognition}, 
  title={Bottom-Up Segmentation for Top-Down Detection}, 
  year={2013},
  volume={},
  number={},
  pages={3294-3301},}
  
  @inproceedings{pspnet,
  title={Pyramid Scene Parsing Network},
  author={Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya},
  booktitle={CVPR},
  year={2017}
}

@inproceedings{auto_deeplab,
  author    = {Chenxi Liu and
               Liang{-}Chieh Chen and
               Florian Schroff and
               Hartwig Adam and
               Wei Hua and
               Alan L. Yuille and
               Fei{-}Fei Li},
  title     = {Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic
               Image Segmentation},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
               2019, Long Beach, CA, USA, June 16-20, 2019},
  pages     = {82--92},
  publisher = {Computer Vision Foundation / {IEEE}},
  year      = {2019},
  url       = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Liu\_Auto-DeepLab\_Hierarchical\_Neural\_Architecture\_Search\_for\_Semantic\_Image\_Segmentation\_CVPR\_2019\_paper.html},
  doi       = {10.1109/CVPR.2019.00017},
  timestamp = {Mon, 20 Jan 2020 15:36:04 +0100},
  biburl    = {https://dblp.org/rec/conf/cvpr/LiuCSAHY019.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{deeplab_v2,
  author    = {Liang{-}Chieh Chen and
               George Papandreou and
               Florian Schroff and
               Hartwig Adam},
  title     = {Rethinking Atrous Convolution for Semantic Image Segmentation},
  journal   = {CoRR},
  volume    = {abs/1706.05587},
  year      = {2017},
  url       = {http://arxiv.org/abs/1706.05587},
  archivePrefix = {arXiv},
  eprint    = {1706.05587},
  timestamp = {Mon, 13 Aug 2018 16:48:07 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/ChenPSA17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{panoptic_deeplab,
  author    = {Bowen Cheng and
               Maxwell D. Collins and
               Yukun Zhu and
               Ting Liu and
               Thomas S. Huang and
               Hartwig Adam and
               Liang{-}Chieh Chen},
  title     = {Panoptic-DeepLab: {A} Simple, Strong, and Fast Baseline for Bottom-Up
               Panoptic Segmentation},
  journal   = {CoRR},
  volume    = {abs/1911.10194},
  year      = {2019},
  url       = {http://arxiv.org/abs/1911.10194},
  archivePrefix = {arXiv},
  eprint    = {1911.10194},
  timestamp = {Tue, 03 Dec 2019 14:15:54 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1911-10194.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{unet,
  added-at = {2020-04-07T14:51:49.000+0200},
  address = {Cham},
  author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  biburl = {https://www.bibsonomy.org/bibtex/2c100345d7fcc0a68ee3e6aa2bf7f305d/tobias.koopmann},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015},
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  interhash = {9158de16b2caff7458df054dc6fc2748},
  intrahash = {c100345d7fcc0a68ee3e6aa2bf7f305d},
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  keywords = {reserved seminar thema thema:image},
  pages = {234--241},
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@inproceedings{fast_scnn,
  author    = {Rudra P. K. Poudel and
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  pages     = {289},
  publisher = {{BMVA} Press},
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    title={ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation},
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    year={2019},
    eprint={1906.09826},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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doi = {10.1007/978-3-030-01234-2_44}
}

@InProceedings{future_seg,
author = {Luc, Pauline and Couprie, Camille and LeCun, Yann and Verbeek, Jakob},
title = {Predicting Future Instance Segmentation by Forecasting Convolutional Features},
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month = {September},
year = {2018}
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@ARTICLE{seg_embed,
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@inproceedings{sem_warp,
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@inproceedings{feelvos2019,
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    year={2019}
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@inproceedings{CVPR2019_CycleTime,
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@InProceedings{cycle_vid_interp,
author = {Reda, Fitsum A. and Sun, Deqing and Dundar, Aysegul and Shoeybi, Mohammad and Liu, Guilin and Shih, Kevin J. and Tao, Andrew and Kautz, Jan and Catanzaro, Bryan},
title = {Unsupervised Video Interpolation Using Cycle Consistency},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
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@inproceedings{pix2pix,
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@inproceedings{spades,
  title={Semantic Image Synthesis with Spatially-Adaptive Normalization},
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  intrahash = {570f7892caafef02eaa010686f893b3f},
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  title = {Understanding deep learning requires rethinking generalization},
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@article{noisy_self,
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@incollection{pseudo_nips_1,
title = {Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation},
author = {Zhang, Qiming and Zhang, Jing and Liu, Wei and Tao, Dacheng},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {435--445},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8335-category-anchor-guided-unsupervised-domain-adaptation-for-semantic-segmentation.pdf}
}
@incollection{domain_seg_nips_1,
title = {Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation},
author = {Zhang, Qiming and Zhang, Jing and Liu, Wei and Tao, Dacheng},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {435--445},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8335-category-anchor-guided-unsupervised-domain-adaptation-for-semantic-segmentation.pdf}
}

@article{gated_iccv2019,
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  year={2019}
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@inproceedings{gal_multitask,
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@inproceedings{optuna_2019,
    title={Optuna: A Next-generation Hyperparameter Optimization Framework},
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    year={2019}
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@misc{alex2019big,
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    year={2019},
    eprint={1912.11370},
    archivePrefix={arXiv},
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@InProceedings{mapillary,
title=    {The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes},
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year=     {2017},
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@InProceedings{deep_v3,
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year = {2018}
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@InProceedings{resnext,
author = {Xie, Saining and Girshick, Ross and Dollar, Piotr and Tu, Zhuowen and He, Kaiming},
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%%% -*-BibTeX-*-
@article{wider_res38,
    author  = {Zifeng Wu    and  Chunhua Shen and Anton {van den Hengel}},
    title   = {Wider or Deeper: Revisiting the {ResNet} Model for Visual Recognition},
    journal = {Pattern Recognition},
    year    = {2019},
    eprint  = {1611.10080},
    venue   = {PR},
}
 

@inproceedings{Yang2018DenseASPP,
	title={{DenseASPP for Semantic Segmentation in Street Scenes}},
	author={Maoke Yang and Kun Yu and Chi Zhang and Zhiwei Li and Kuiyuan Yang},
	booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year={2018}
}

@InProceedings{Cheng2019SPGNet,
author = {Cheng, Bowen and Chen, Liang-Chieh and Wei, Yunchao and Zhu, Yukun and Huang, Zilong and Xiong, Jinjun and Huang, Thomas S. and Hwu, Wen-Mei and Shi, Honghui},
title = {{SPGNet: Semantic Prediction Guidance for Scene Parsing}},
booktitle = { IEEE International Conference on Computer Vision (ICCV)},
year = {2019}
}


@InProceedings{Fu2019ACNet,
author = {Fu, Jun and Liu, Jing and Wang, Yuhang and Li, Yong and Bao, Yongjun and Tang, Jinhui and Lu, Hanqing},
title = {{Adaptive Context Network for Scene Parsing}},
booktitle = { IEEE International Conference on Computer Vision (ICCV)},
year = {2019}
}
@InProceedings{Li2019EMANet,
author = {Li, Xia and Zhong, Zhisheng and Wu, Jianlong and Yang, Yibo and Lin, Zhouchen and Liu, Hong},
title = {{Expectation-Maximization Attention Networks for Semantic Segmentation}},
booktitle = { IEEE International Conference on Computer Vision (ICCV)},
year = {2019}
}

@article{Sun2019HRNet,
	title={{High-Resolution Representations for Labeling Pixels and Regions}},
	author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
	journal={arXiv preprint arXiv:1904.04514},
	year={2019}
}

@inproceedings{Fu2018DANet,
  title={{Dual Attention Network for Scene Segmentation}},
  author={Jun Fu and Jing Liu and Haijie Tian and Yong Li and Yongjun Bao and Zhiwei Fang and Hanqing Lu},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}


@inproceedings{benenson2019large,
  title={Large-scale interactive object segmentation with human annotators},
  author={Benenson, Rodrigo and Popov, Stefan and Ferrari, Vittorio},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={11700--11709},
  year={2019}
}

@article{kuznetsova2018open,
  title={The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale},
  author={Kuznetsova, Alina and Rom, Hassan and Alldrin, Neil and Uijlings, Jasper and Krasin, Ivan and Pont-Tuset, Jordi and Kamali, Shahab and Popov, Stefan and Malloci, Matteo and Duerig, Tom and others},
  journal={arXiv preprint arXiv:1811.00982},
  year={2018}
}