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Large-scale image databases such as ImageNet have significantly advanced image classification and other visual recognition tasks.
Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
S. Lazebnik, C. Schmid, and J. Ponce · 2006
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Nus-wide: A real-world web image database from national university of singapore
T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y.-T. Zheng · 2009
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li · 2009
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Recognizing indoor scenes
A. Quattoni and A. Torralba · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
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Novel dataset for fine-grained image categorization: Stanford dogs
A. Khosla, N. Jayadevaprakash, B. Yao, and F.-F. Li · 2011
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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Articulated pose estimation with flexible mixtures-of-parts
Y. Yang and D. Ramanan · 2011
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Apparel classification with style
L. Bossard, M. Dantone, C. Leistner, C. Wengert, T. Quack, and L. Van Gool · 2012
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Describing clothing by semantic attributes
H. Chen, A. Gallagher, and B. Girod · 2012
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Parsing clothing in fashion photographs
K. Yamaguchi, M. H. Kiapour, L. E. Ortiz, and T. L. Berg · 2012
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3d object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
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Fine-grained visual classification of aircraft
S. Maji, E. Rahtu, J. Kannala, M. Blaschko, and A. Vedaldi · 2013
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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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The pascal visual object classes challenge: A retrospective
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2015
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Where to buy it: Matching street clothing photos in online shops
M. Hadi Kiapour, X. Han, S. Lazebnik, A. C. Berg, and T. L. Berg · 2015
Cited alongside, same era.
Decoupled deep neural network for semi-supervised semantic segmentation
S. Hong, H. Noh, and B. Han · 2015
Cited alongside, same era.
Cross-domain image retrieval with a dual attribute-aware ranking network
J. Huang, R. S. Feris, Q. Chen, and S. Yan · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
A unified view of multi-label performance measures
X.-Z. Wu and Z.-H. Zhou · 2016
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Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Cleannet: Transfer learning for scalable image classifier training with label noise
K.-H. Lee, X. He, L. Zhang, and L. Yang · 2017
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Webvision database: Visual learning and understanding from web data
W. Li, L. Wang, W. Li, E. Agustsson, and L. Van Gool · 2017
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Improving pairwise ranking for multi-label image classification
Y. Li, Y. Song, and J. Luo · 2017
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S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang · 2015
Cited alongside, same era.
Deep residual learning for image recognition, 2016
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Cited alongside, same era.
Deepfashion: Powering robust clothes recognition and retrieval with rich annotations
Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Making deep neural networks robust to label noise: A loss correction approach
G. Patrini, A. Rozza, A. K. Menon, R. Nock, and L. Qu · 2017
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Multi-label image recognition by recurrently discovering attentional regions
Z. Wang, T. Chen, G. Li, R. Xu, and L. Lin · 2017
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Places: A 10 million image database for scene recognition
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba · 2017
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Learning spatial regularization with image-level supervisions for multi-label image classification
F. Zhu, H. Li, W. Ouyang, N. Yu, and X. Wang · 2017
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Curriculumnet: Weakly supervised learning from large-scale web images
S. Guo, W. Huang, H. Zhang, C. Zhuang, D. Dong, M. R. Scott, and D. Huang · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, T. Duerig, et al · 2018
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2018
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The inaturalist species classification and detection dataset
G. Van Horn, O. Mac Aodha, Y. Song, Y. Cui, C. Sun, A. Shepard, H. Adam, P. Perona, and S. Belongie · 2018
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