Scale-Aware Domain Adaptive Faster R-CNN
暂无分享,去创建一个
Luc Van Gool | Dengxin Dai | Wen Li | Christos Sakaridis | Yuhua Chen | Haoran Wang | L. Gool | Dengxin Dai | Wen Li | Christos Sakaridis | Yuhua Chen | Haoran Wang
[1] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[2] Zhiqiang Shen,et al. SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses , 2019, ArXiv.
[3] Rama Chellappa,et al. Wasserstein Distance Based Domain Adaptation for Object Detection , 2019, ArXiv.
[4] Changick Kim,et al. Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[5] Yizhou Wang,et al. Multi-Level Domain Adaptive Learning for Cross-Domain Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[6] Lei Zhang,et al. Multi-Adversarial Faster-RCNN for Unrestricted Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[7] Xinge Zhu,et al. Adapting Object Detectors via Selective Cross-Domain Alignment , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Changick Kim,et al. Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Chong-Wah Ngo,et al. Exploring Object Relation in Mean Teacher for Cross-Domain Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Liangliang Cao,et al. Automatic Adaptation of Object Detectors to New Domains Using Self-Training , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Arash Vahdat,et al. A Robust Learning Approach to Domain Adaptive Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[12] Dacheng Tao,et al. Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Luc Van Gool,et al. DLOW: Domain Flow for Adaptation and Generalization , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Kate Saenko,et al. Strong-Weak Distribution Alignment for Adaptive Object Detection , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Luc Van Gool,et al. Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Chee-Meng Chew,et al. Pixel and Feature Level Based Domain Adaption for Object Detection in Autonomous Driving , 2018, Neurocomputing.
[17] Trevor Darrell,et al. SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection , 2018, ArXiv.
[18] Wen Li,et al. Domain Generalization and Adaptation Using Low Rank Exemplar SVMs , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Kiyoharu Aizawa,et al. Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[20] Luc Van Gool,et al. Domain Adaptive Faster R-CNN for Object Detection in the Wild , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[21] Ming-Hsuan Yang,et al. Learning to Adapt Structured Output Space for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[22] Luc Van Gool,et al. ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[23] Luc Van Gool,et al. Semantic Foggy Scene Understanding with Synthetic Data , 2017, International Journal of Computer Vision.
[24] Harshad Rai,et al. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks , 2018 .
[25] Yongxin Yang,et al. Deeper, Broader and Artier Domain Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[26] Juergen Gall,et al. Open Set Domain Adaptation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[27] Donald A. Adjeroh,et al. Unified Deep Supervised Domain Adaptation and Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[28] Timnit Gebru,et al. Fine-Grained Recognition in the Wild: A Multi-task Domain Adaptation Approach , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[29] Daniel Cremers,et al. Associative Domain Adaptation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[30] Philip David,et al. Domain Adaptation for Semantic Segmentation of Urban Scenes , 2017 .
[31] Zhiguo Cao,et al. When Unsupervised Domain Adaptation Meets Tensor Representations , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[32] Fabio Maria Carlucci,et al. AutoDIAL: Automatic Domain Alignment Layers , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[33] Ping Tan,et al. DualGAN: Unsupervised Dual Learning for Image-to-Image Translation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[34] Hyunsoo Kim,et al. Learning to Discover Cross-Domain Relations with Generative Adversarial Networks , 2017, ICML.
[35] Jan Kautz,et al. Unsupervised Image-to-Image Translation Networks , 2017, NIPS.
[36] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Matthew Johnson-Roberson,et al. Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks? , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[38] Derong Liu,et al. Neural Information Processing , 2017, Lecture Notes in Computer Science.
[39] Trevor Darrell,et al. FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation , 2016, ArXiv.
[40] Liang Lin,et al. Is Faster R-CNN Doing Well for Pedestrian Detection? , 2016, ECCV.
[41] Mengjie Zhang,et al. Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation , 2016, ECCV.
[42] Luc Van Gool,et al. Scale-Aware Alignment of Hierarchical Image Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Yi Li,et al. R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.
[44] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[46] Jian Sun,et al. Instance-Aware Semantic Segmentation via Multi-task Network Cascades , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[48] Kate Saenko,et al. Return of Frustratingly Easy Domain Adaptation , 2015, AAAI.
[49] Silvio Savarese,et al. Learning Transferrable Representations for Unsupervised Domain Adaptation , 2016, NIPS.
[50] Tinne Tuytelaars,et al. Subspace Alignment Based Domain Adaptation for RCNN Detector , 2015, BMVC.
[51] Takeo Kanade,et al. Learning scene-specific pedestrian detectors without real data , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[52] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[53] Nikos Komodakis,et al. Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[54] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[55] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[56] Kate Saenko,et al. Learning Deep Object Detectors from 3D Models , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[57] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[58] Jiaolong Xu,et al. Domain Adaptation of Deformable Part-Based Models , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[59] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[60] Xiang Zhang,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[61] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[62] Kate Saenko,et al. From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains , 2014, BMVC.
[63] Tinne Tuytelaars,et al. Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.
[64] Andreas Geiger,et al. Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..
[65] Koen E. A. van de Sande,et al. Selective Search for Object Recognition , 2013, International Journal of Computer Vision.
[66] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[67] Fei-Fei Li,et al. Shifting Weights: Adapting Object Detectors from Image to Video , 2012, NIPS.
[68] Derek Hoiem,et al. Diagnosing Error in Object Detectors , 2012, ECCV.
[69] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[70] Ivor W. Tsang,et al. Domain Transfer Multiple Kernel Learning , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[71] Rama Chellappa,et al. Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.
[72] Alexei A. Efros,et al. Unbiased look at dataset bias , 2011, CVPR 2011.
[73] Trevor Darrell,et al. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms , 2011, CVPR 2011.
[74] Ivor W. Tsang,et al. Visual Event Recognition in Videos by Learning from Web Data , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[75] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[76] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[77] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[78] Dima Damen,et al. Recognizing linked events: Searching the space of feasible explanations , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[79] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[80] Shree K. Nayar,et al. Vision and the Atmosphere , 2002, International Journal of Computer Vision.
[81] Paul A. Viola,et al. Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.