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Performing data augmentation for learning deep neural networks is well known to be important for training visual recognition systems.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: · 1958
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Statistical context priming for object detection
Torralba, A., Sinha, P.: · 2001
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Contextual priming for object detection
Torralba, A.: · 2003
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Poisson image editing
Pérez, P., Gangnet, M., Blake, A.: · 2003
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Object detection and localization using local and global features
Murphy, K., Torralba, A., Eaton, D., Freeman, W.: · 2006
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The role of context in object recognition
Oliva, A., Torralba, A.: · 2007
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Learning spatial context: Using stuff to find things
Heitz, G., Koller, D.: · 2008
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An empirical study of context in object detection
Divvala, S.K., Hoiem, D., Hays, J.H., Efros, A.A., Hebert, M.: · 2009
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Decomposing a scene into geometric and semantically consistent regions
Gould, S., Fulton, R., Koller, D.: · 2009
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Object detection with discriminatively trained part-based models
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: · 2010
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Multiresolution models for object detection
Park, D., Ramanan, D., Fowlkes, C.: · 2010
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The PASCAL visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: · 2010
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Exploiting hierarchical context on a large database of object categories
Choi, M.J., Lim, J.J., Torralba, A., Willsky, A.S.: · 2010
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Modeling mutual context of object and human pose in human-object interaction activities
Yao, B., Fei-Fei, L.: · 2010
Cited alongside, same era.
Rendering synthetic objects into legacy photographs
Karsch, K., Hedau, V., Forsyth, D., Hoiem, D.: · 2011
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Building a dictionary of image fragments
Liao, Z., Farhadi, A., Wang, Y., Endres, I., Forsyth, D.: · 2012
Cited alongside, same era.
Microsoft COCO: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
Cited alongside, same era.
Fast R-CNN
Girshick, R.: · 2015
Cited alongside, same era.
Synthetic data for text localisation in natural images
Gupta, A., Vedaldi, A., Zisserman, A.: · 2016
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
Bell, S., Zitnick, C.L., Bala, K., Girshick, R.: · 2016
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How useful is photo-realistic rendering for visual learning?
Movshovitz-Attias, Y., Kanade, T., Sheikh, Y.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Cut, paste and learn: Surprisingly easy synthesis for instance detection
Dwibedi, D., Misra, I., Hebert, M.: · 2017
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Synthesizing training data for object detection in indoor scenes
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ImageNet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
Cited alongside, same era.
Learning deep object detectors from 3d models
Peng, X., Sun, B., Ali, K., Saenko, K.: · 2015
Cited alongside, same era.
Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
Su, H., Qi, C.R., Li, Y., Guibas, L.J.: · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D., Ba, J.: · 2015
Cited alongside, same era.
SSD: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: · 2016
Cited alongside, same era.
Georgakis, G., Mousavian, A., Berg, A.C., Kosecka, J.: · 2017
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Blitznet: A real-time deep network for scene understanding
Dvornik, N., Shmelkov, K., Mairal, J., Schmid, C.: · 2017
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DSSD: Deconvolutional single shot detector
Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., Berg, A.C.: · 2017
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On the utility of context (or the lack thereof) for object detection
Barnea, E., Ben-Shahar, O.: · 2017
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Random erasing data augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: · 2017
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Focal loss for dense object detection
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: · 2017
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Deep feature based contextual model for object detection
Chu, W., Cai, D.: · 2018
Closest in time.
Synthetic data augmentation using gan for improved liver lesion classification
Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: · 2018
Closest in time.