MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation
暂无分享,去创建一个
Vladlen Koltun | Ozan Sener | Zhuang Liu | James Hays | John Lambert | Zhuang Liu | James Hays | V. Koltun | Zhuang Liu | John Lambert | Ozan Sener
[1] Iasonas Kokkinos,et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.
[2] Dong Liu,et al. High-Resolution Representations for Labeling Pixels and Regions , 2019, ArXiv.
[3] Silvio Savarese,et al. Generalizing to Unseen Domains via Adversarial Data Augmentation , 2018, NeurIPS.
[4] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[5] Wei Wu,et al. Feedback Network for Image Super-Resolution , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Matthias Nießner,et al. ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Jianxiong Xiao,et al. SUN RGB-D: A RGB-D scene understanding benchmark suite , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Vladlen Koltun,et al. Playing for Benchmarks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[9] Donald A. Adjeroh,et al. Unified Deep Supervised Domain Adaptation and Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[10] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[11] Vladlen Koltun,et al. Photographic Image Synthesis with Cascaded Refinement Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[12] Gijs Dubbelman,et al. Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).
[13] Alexei A. Efros,et al. Unbiased look at dataset bias , 2011, CVPR 2011.
[14] Antonio Criminisi,et al. TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context , 2007, International Journal of Computer Vision.
[15] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] H. Shimodaira,et al. Improving predictive inference under covariate shift by weighting the log-likelihood function , 2000 .
[17] Marin Oršić,et al. Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift , 2019, GCPR.
[18] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[19] Davide Mazzini,et al. Training Efficient Semantic Segmentation CNNs on Multiple Datasets , 2019, ICIAP.
[20] Derek Hoiem,et al. Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.
[21] Barbara Caputo,et al. Robust Place Categorization With Deep Domain Generalization , 2018, IEEE Robotics and Automation Letters.
[22] Andrea Vedaldi,et al. Universal representations: The missing link between faces, text, planktons, and cat breeds , 2017, ArXiv.
[23] Hugo Larochelle,et al. Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples , 2019, ICLR.
[24] Oliver Zendel,et al. WildDash - Creating Hazard-Aware Benchmarks , 2018, ECCV.
[25] Vladlen Koltun,et al. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.
[26] Andrea Vedaldi,et al. Learning multiple visual domains with residual adapters , 2017, NIPS.
[27] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[28] Brendan T. O'Connor,et al. Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.
[29] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] C. V. Jawahar,et al. IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments , 2018, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).
[31] Vittorio Ferrari,et al. COCO-Stuff: Thing and Stuff Classes in Context , 2016, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[32] Andreas Geiger,et al. Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..
[33] Konrad Schindler,et al. Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] J. Heckman. Sample selection bias as a specification error , 1979 .
[35] Roberto Cipolla,et al. Semantic object classes in video: A high-definition ground truth database , 2009, Pattern Recognit. Lett..
[36] Peter Kontschieder,et al. The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[37] Germán Ros,et al. Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation , 2016, ArXiv.
[38] Seymour A. Papert,et al. The Summer Vision Project , 1966 .
[39] Sanja Fidler,et al. The Role of Context for Object Detection and Semantic Segmentation in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[40] C. V. Jawahar,et al. Universal Semi-Supervised Semantic Segmentation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[41] Xiaogang Wang,et al. Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Ross B. Girshick,et al. LVIS: A Dataset for Large Vocabulary Instance Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Bolei Zhou,et al. Semantic Understanding of Scenes Through the ADE20K Dataset , 2016, International Journal of Computer Vision.
[44] Vladlen Koltun,et al. Multi-Task Learning as Multi-Objective Optimization , 2018, NeurIPS.
[45] Eric P. Xing,et al. Dynamic-Structured Semantic Propagation Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.