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Recent region-based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks.
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., Schapire, R.E.: · 1997
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Robust real-time face detection
Viola, P., Jones, M.J.: · 2004
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Robust object detection via soft cascade
Bourdev, L., Brandt, J.: · 2005
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2010
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Object detection with discriminatively trained part-based models
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: · 2010
Earlier work this paper cites.
Diagnosing error in object detectors
Hoiem, D., Chodpathumwan, Y., Dai, Q.: · 2012
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Selective search for object recognition
Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: · 2013
Earlier work this paper cites.
Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Spatial pyramid pooling in deep convolutional networks for visual recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2014
Earlier work this paper cites.
Fast r-cnn
Girshick, R.: · 2015
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.
Towards computational baby learning: A weakly-supervised approach for object detection
Liang, X., Liu, S., Wei, Y., Liu, L., Lin, L., Yan, S.: · 2015
Cited alongside, same era.
A convolutional neural network cascade for face detection
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: · 2015
Cited alongside, same era.
R-fcn: Object detection via region-based fully convolutional networks
Dai, J., Li, Y., He, K., Sun, J.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Feature pyramid networks for object detection
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: · 2017
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Deformable convolutional networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: · 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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Yolo9000: Better, faster, stronger
Redmon, J., Farhadi, A.: · 2017
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Accurate, large minibatch sgd: training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., He, K.: · 2017
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Mask r-cnn
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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.
Training region-based object detectors with online hard example mining
Shrivastava, A., Gupta, A., Girshick, R.: · 2016
Cited alongside, same era.
Deep regionlets for object detection
Xu, H., Lv, X., Wang, X., Ren, Z., Chellappa, R.: · 2017
Cited alongside, same era.
Attentive contexts for object detection
Li, J., Wei, Y., Liang, X., Dong, J., Xu, T., Feng, J., Yan, S.: · 2017
Cited alongside, same era.
Perceptual generative adversarial networks for small object detection
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: · 2017
Cited alongside, same era.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: · 2017
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Focal loss for dense object detection
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: · 2017
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Cascade r-cnn: Delving into high quality object detection
Cai, Z., Vasconcelos, N.: · 2018
Closest in time.
Multistage object detection with group recursive learning
Li, J., Liang, X., Li, J., Wei, Y., Xu, T., Feng, J., Yan, S.: · 2018
Closest in time.
Ts2c: Tight box mining with surrounding segmentation context for weakly supervised object detection
Wei, Y., Shen, Z., Cheng, B., Shi, H., Xiong, J., Feng, J., Huang, T.: · 2018
Closest in time.