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We propose an approach for unsupervised adaptation of object detectors from label-rich to label-poor domains which can significantly reduce annotation costs associated with detection.
Analysis of representations for domain adaptation
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The pascal visual object classes (voc) challenge
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Unsupervised domain adaptation by backpropagation
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Weakly supervised deep detection networks
H. Bilen and A. Vedaldi · 2016
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The cityscapes dataset for semantic urban scene understanding
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Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?
M. Johnson-Roberson, C. Barto, R. Mehta, S. N. Sridhar, K. Rosaen, and R. Vasudevan · 2016
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Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
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Unsupervised domain adaptation with residual transfer networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2016
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You only look once: Unified, real-time object detection
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, et al · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Domain adaptive faster r-cnn for object detection in the wild
Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool · 2018
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Cycada: Cycle-consistent adversarial domain adaptation
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. A. Efros, and T. Darrell · 2018
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Cross-domain weakly-supervised object detection through progressive domain adaptation, 2018
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
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Large scale semi-supervised object detection using visual and semantic knowledge transfer
Y. Tang, J. Wang, B. Gao, E. Dellandréa, R. Gaizauskas, and L. Chen · 2016
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Open set domain adaptation
P. P. Busto and J. Gall · 2017
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Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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Unsupervised image-to-image translation networks
M.-Y. Liu, T. Breuel, and J. Kautz · 2017
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Least squares generative adversarial networks
X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. P. Smolley · 2017
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N. Inoue, R. Furuta, T. Yamasaki, and K. Aizawa · 2018
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Conditional adversarial domain adaptation
M. Long, Z. Cao, J. Wang, and M. I. Jordan · 2018
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Adversarial dropout regularization
K. Saito, Y. Ushiku, T. Harada, and K. Saenko · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
K. Saito, K. Watanabe, Y. Ushiku, and T. Harada · 2018
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Open set domain adaptation by backpropagation
K. Saito, S. Yamamoto, Y. Ushiku, and T. Harada · 2018
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Semantic foggy scene understanding with synthetic data
C. Sakaridis, D. Dai, and L. Van Gool · 2018
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Learning from synthetic data: Addressing domain shift for semantic segmentation
S. Sankaranarayanan, Y. Balaji, A. Jain, S. N. Lim, and R. Chellappa · 2018
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Importance weighted adversarial nets for partial domain adaptation
J. Zhang, Z. Ding, W. Li, and P. Ogunbona · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Y. Zou, Z. Yu, B. V. Kumar, and J. Wang · 2018
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