Fetching the paper…
Reading the bibliography…
Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice.
Rapid object detection using a boosted cascade of simple features
P. Viola and M. Jones · 2001
Earlier work this paper cites.
Vision and the atmosphere
S. G. Narasimhan and S. K. Nayar · 2002
Earlier work this paper cites.
Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
Earlier work this paper cites.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
Earlier work this paper cites.
The Pascal visual object classes (VOC) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2010
Earlier work this paper cites.
Domain adaptation for object recognition: An unsupervised approach
R. Gopalan, R. Li, and R. Chellappa · 2011
Earlier work this paper cites.
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
B. Kulis, K. Saenko, and T. Darrell · 2011
Earlier work this paper cites.
Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
Earlier work this paper cites.
Domain transfer multiple kernel learning
L. Duan, I. W. Tsang, and D. Xu · 2012
Earlier work this paper cites.
Visual event recognition in videos by learning from web data
L. Duan, D. Xu, I. W. Tsang, and J. Luo · 2012
Earlier work this paper cites.
Geodesic flow kernel for unsupervised domain adaptation
B. Gong, Y. Shi, F. Sha, and K. Grauman · 2012
Earlier work this paper cites.
Diagnosing error in object detectors
D. Hoiem, Y. Chodpathumwan, and Q. Dai · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Shifting weights: Adapting object detectors from image to video
K. Tang, V. Ramanathan, L. Fei-Fei, and D. Koller · 2012
Earlier work this paper cites.
Unsupervised visual domain adaptation using subspace alignment
B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars · 2013
Earlier work this paper cites.
Vision meets robotics: The KITTI dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
Earlier work this paper cites.
OverFeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
From virtual to reality: Fast adaptation of virtual object detectors to real domains
B. Sun and K. Saenko · 2014
Cited alongside, same era.
Domain adaptation of deformable part-based models
J. Xu, S. Ramos, D. Vázquez, and A. M. Lopez · 2014
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
Cited alongside, same era.
Object detection via a multi-region and semantic segmentation-aware CNN model
S. Gidaris and N. Komodakis · 2015
Cited alongside, same era.
Fast R-CNN
R. Girshick · 2015
Cited alongside, same era.
Learning scene-specific pedestrian detectors without real data
H. Hattori, V. Naresh Boddeti, K. M. Kitani, and T. Kanade · 2015
Cited alongside, same era.
Learning transferrable representations for unsupervised domain adaptation
O. Sener, H. O. Song, A. Saxena, and S. Savarese · 2016
Later among the works it cites.
Return of frustratingly easy domain adaptation
B. Sun, J. Feng, and K. Saenko · 2016
Later among the works it cites.
Is faster R-CNN doing well for pedestrian detection?
L. Zhang, L. Lin, X. Liang, and K. He · 2016
Later among the works it cites.
Fine-grained recognition in the wild: A multi-task domain adaptation approach
T. Gebru, J. Hoffman, and L. Fei-Fei · 2017
Later among the works it cites.
Associative domain adaptation
P. Haeusser, T. Frerix, A. Mordvintsev, and D. Cremers · 2017
Later among the works it cites.
Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
Cited alongside, same era.
Learning deep object detectors from 3D models
X. Peng, B. Sun, K. Ali, and K. Saenko · 2015
Cited alongside, same era.
Subspace alignment based domain adaptation for RCNN detector
A. Raj, V. P. Namboodiri, and T. Tuytelaars · 2015
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Attention to scale: Scale-aware semantic image segmentation
L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille · 2016
Cited alongside, same era.
Scale-aware alignment of hierarchical image segmentation
Y. Chen, D. Dai, J. Pont-Tuset, and L. Van Gool · 2016
Cited alongside, same era.
M. Johnson-Roberson, C. Barto, R. Mehta, S. N. Sridhar, K. Rosaen, and R. Vasudevan · 2017
Later among the works it cites.
Learning to discover cross-domain relations with generative adversarial networks
T. Kim, M. Cha, H. Kim, J. Lee, and J. Kim · 2017
Later among the works it cites.
Deeper, broader and artier domain generalization
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales · 2017
Later among the works it cites.
Domain generalization and adaptation using low rank exemplar SVMs
W. Li, Z. Xu, D. Xu, D. Dai, and L. Van Gool · 2017
Later among the works it cites.
Unsupervised image-to-image translation networks
M.-Y. Liu, T. Breuel, and J. Kautz · 2017
Later among the works it cites.
When unsupervised domain adaptation meets tensor representations
H. Lu, L. Zhang, Z. Cao, W. Wei, K. Xian, C. Shen, and A. van den Hengel · 2017
Later among the works it cites.
AutoDIAL: Automatic domain alignment layers
F. Maria Carlucci, L. Porzi, B. Caputo, E. Ricci, and S. Rota Bulo · 2017
Later among the works it cites.
Unified deep supervised domain adaptation and generalization
S. Motiian, M. Piccirilli, D. A. Adjeroh, and G. Doretto · 2017
Later among the works it cites.
Open set domain adaptation
P. Panareda Busto and J. Gall · 2017
Later among the works it cites.
Deep domain adaptation by geodesic distance minimization
Y. Wang, W. Li, D. Dai, and L. Van Gool · 2017
Later among the works it cites.
DualGAN: Unsupervised dual learning for image-to-image translation
Z. Yi, H. Zhang, P. T. Gong, et al · 2017
Later among the works it cites.
Curriculum domain adaptation for semantic segmentation of urban scenes
Y. Zhang, P. David, and B. Gong · 2017
Later among the works it cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Later among the works it cites.
ROAD: Reality oriented adaptation for semantic segmentation of urban scenes
Y. Chen, W. Li, and L. Van Gool · 2018
Closest in time.
Semantic foggy scene understanding with synthetic data
C. Sakaridis, D. Dai, and L. Van Gool · 2018
Closest in time.