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Transfer learning enables to re-use knowledge learned on a source task to help learning a target task.
A new measure of rank correlation
M. Kendall · 1938
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Semantic object classes in video: A high-definition ground truth database
G. J. Brostow, J. Fauqueur, and R. Cipolla · 2009
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Adapting visual category models to new domains
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A category-level 3-d object dataset: Putting the kinect to work
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Novel dataset for fine-grained image categorization
A. Khosla, N. Jayadevaprakash, B. Yao, and L. Fei-Fei · 2011
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Imagenet large scale visual recognition challenge (ILSVRC) · 2012
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
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Indoor segmentation and support inference from rgbd images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
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Distance-based image classification: Generalizing to new classes at near-zero cost
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka · 2013
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SUN3D: A database of big spaces reconstructed using SfM and object labels
J. Xiao, A. Owens, and A. Torralba · 2013
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2d human pose estimation: New benchmark and state of the art analysis
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele · 2014
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2014
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Pulling things out of perspective
L. Ladicky, J. Shi, and M. Pollefeys · 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. Zitnick · 2014
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The role of context for object detection and semantic segmentation in the wild
R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille · 2014
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Theme section — urban object detection and 3d building reconstruction
F. Rottensteiner, G. Sohn, M. Gerke, and J. D. Wegner · 2014
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Factors of transferability for a generic convnet representation
H. Azizpour, A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson · 2015
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The PASCAL visual object classes challenge: A retrospective
M. Everingham, S. Eslami, L. van Gool, C. Williams, J. Winn, and A. Zisserman · 2015
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Fast R-CNN
R. Girshick · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. L. Ba · 2015
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Siamese neural networks for one-shot image recognition
G. Koch, R. Zemel, and R. Salakhutdinov · 2015
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. 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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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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SUN RGB-D: A RGB-D scene understanding benchmark suite
S. Song, S. Lichtenberg, and J. Xiao · 2015
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Best practices for fine-tuning visual classifiers to new domains
B. Chu, V. Madhavan, O. Beijbom, J. Hoffman, and T. Darrell · 2016
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Virtual worlds as proxy for multi-object tracking analysis
A. Gaidon, Q. Wang, Y. Cabon, and E. Vig · 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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Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
J. Hoffman, D. Wang, F. Yu, and T. Darrell · 2016
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What makes imagenet good for transfer learning?
M. Huh, P. Agrawal, and A. Efros · 2016
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Learning visual features from large weakly supervised data
A. Joulin, L. van der Maaten, A. Jabri, and N. Vasilache · 2016
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Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2016
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Fully convolutional networks for semantic segmentation
E. Shelhamer, J. Long, and T. Darrell · 2016
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Deep coral: Correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
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Semantic instance annotation of urban scenes by 3d to 2d label transfer
J. Xie, M. Kiefel, M.-T. Sun, and A. Geiger · 2016
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Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
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Unsupervised pixel-level domain adaptation with generative adversarial networks
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2017
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ScanNet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning
W. Ge and Y. Yu · 2017
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Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
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Ubernet: Training a ‘universal’ cnn for low-, mid-, and high- level vision using diverse datasets and limited memory
Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras
A. Gordon, H. Li, R. Jonschkowski, and A. Angelova · 2019
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S. Huang and D. Tao · 2019
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Panoptic segmentation
A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár · 2019
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Do better imagenet models transfer better?
S. Kornblith, J. Shlens, and Q. V. Le · 2019
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Unsupervised domain adaptation using feature-whitening and consensus loss
S. Roy, A. Siarohin, E. Sangineto, S. R. Bulo, N. Sebe, and E. Ricci · 2019
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I. Kokkinos · 2017
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A two-streamed network for estimating fine-scaled depth maps from single RGB images
J. Li, R. Klein, and A. Yao · 2017
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Cited alongside, same era.
AutoDIAL: Automatic DomaIn Alignment Layers
F. Maria Carlucci, L. Porzi, B. Caputo, E. Ricci, and S. Rota Bulo · 2017
Cited alongside, same era.
The mapillary vistas dataset for semantic understanding of street scenes
G. Neuhold, T. Ollmann, S. Rota Bulò, and P. Kontschieder · 2017
Cited alongside, same era.
Learning multiple visual domains with residual adapters
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 2017
Cited alongside, same era.
One-shot learning for semantic segmentation
A. Shaban, S. Bansal, Z. Liu, I. Essa, and B. Boots · 2017
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Y. Sun, E. Tzeng, T. Darrell, and A. A. Efros · 2019
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Idd: A dataset for exploring problems of autonomous navigation in unconstrained environments
G. Varma, A. Subramanian, A. Namboodiri, M. Chandraker, and C. Jawahar · 2019
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Panet: Few-shot image semantic segmentation with prototype alignment
K. Wang, J. H. Liew, Y. Zou, D. Zhou, and J. Feng · 2019
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isaid: A large-scale dataset for instance segmentation in aerial images
S. Waqas Zamir, A. Arora, A. Gupta, S. Khan, G. Sun, F. Shahbaz Khan, F. Zhu, L. Shao, G.-S. Xia, and X. Bai · 2019
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Visual localization by learning objects-of-interest dense match regression
P. Weinzaepfel, G. Csurka, Y. Cabon, and M. Humenberger · 2019
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A large-scale study of representation learning with the visual task adaptation benchmark
X. Zhai, J. Puigcerver, A. Kolesnikov, P. Ruyssen, C. Riquelme, M. Lucic, J. Djolonga, A. S. Pinto, M. Neumann, A. Dosovitskiy, L. Beyer, O. Bachem, M. Tschannen, M. Michalski, O. Bousquet, S. Gelly, and N. Houlsby · 2019
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Human pose estimation with spatial contextual information
H. Zhang, H. Ouyang, S. Liu, X. Qi, X. Shen, R. Yang, and J. Jia · 2019
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Objects as points
X. Zhou, D. Wang, and P. Krähenbühl · 2019
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Learning object-specific distance from a monocular image
J. Zhu and Y. Fang · 2019
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Who left the dogs out?: 3D animal reconstruction with expectation maximization in the loop
B. Biggs, O. Boyne, J. Charles, A. Fitzgibbon, and R. Cipolla · 2020
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Virtual kitti 2, 2020
Y. Cabon, N. Murray, and M. Humenberger · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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Big self-supervised models are strong semi-supervised learners
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. Hinton · 2020
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Sequential mastery of multiple visual tasks: Networks naturally learn to learn and forget to forget
G. Davidson and M. C. Mozer · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
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Trash-icra19: A bounding box labeled dataset of underwater trash
M. Fulton, J. Hong, and J. Sattar · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar, B. Piot, K. Kavukcuoglu, R. Munos, and M. Valko · 2020
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Training neural networks to produce compatible features
M. Gygli, J. Uijlings, and V. Ferrari · 2020
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Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
M. J. Islam, C. Edge, Y. Xiao, P. Luo, M. Mehtaz, C. Morse, S. S. Enan, and J. Sattar · 2020
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Big Transfer (BiT): general visual representation learning
A. Kolesnikov, L. Beyer, X. Zhai, J. Puigcerver, J. Yung, S. Gelly, and N. Houlsby · 2020
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Towards inheritable models for open-set domain adaptation
J. N. Kundu, N. Venkat, A. Revanur, R. V. Babu, et al · 2020
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The open images dataset v4
A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov, et al · 2020
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MSeg: A composite dataset for multi-domain semantic segmentation
J. Lambert, Z. Liu, O. Sener, J. Hays, and V. Koltun · 2020
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Unsupervised domain adaptation for semantic segmentation by content transfer
S. Lee, J. Hyun, H. Seong, and E. Kim · 2020
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Scalable transfer learning with expert models
J. Puigcerver, C. Riquelme, B. Mustafa, C. Renggli, A. S. Pinto, S. Gelly, D. Keysers, and N. Houlsby · 2020
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Rapid learning or feature reuse? towards understanding the effectiveness of MAML
A. Raghu, M. Raghu, S. Bengio, and O. Vinyals · 2020
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When does self-supervision improve few-shot learning?
J.-C. Su, S. Maji, and B. Hariharan · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Y. Tian, Y. Wang, D. Krishnan, J. B. Tenenbaum, and P. Isola · 2020
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Deep high-resolution representation learning for visual recognition
J. Wang, K. Sun, T. Cheng, B. Jiang, C. Deng, Y. Zhao, D. Liu, Y. Mu, M. Tan, X. Wang, W. Liu, and B. Xiao · 2020
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Frustratingly simple few-shot object detection
X. Wang, T. Huang, J. Gonzalez, T. Darrell, and F. Yu · 2020
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Multi-scale positive sample refinement for few-shot object detection
J. Wu, S. Liu, D. Huang, and Y. Wang · 2020
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Neural data server: A large-scale search engine for transfer learning data
X. Yan, D. Acuna, and S. Fidler · 2020
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Bdd100k: A diverse driving dataset for heterogeneous multitask learning
F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell · 2020
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Rethinking pre-training and self-training
B. Zoph, G. Ghiasi, T.-Y. Lin, Y. Cui, H. Liu, E. D. Cubuk, and Q. V. Le · 2020
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Exploring simple siamese representation learning
X. Chen and K. He · 2021
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How well do self-supervised models transfer?
L. Ericsson, H. Gouk, and T. M. Hospedales · 2021
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Supervised transfer learning at scale for medical imaging
B. Mustafa, A. Loh, J. Freyberg, P. MacWilliams, A. Karthikesalingam, N. Houlsby, and V. Natarajan · 2021
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Dast: Unsupervised domain adaptation in semantic segmentation based on discriminator attention and self-training
F. Yu, M. Zhang, H. Dong, S. Hu, B. Dong, and L. Zhang · 2021
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