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In this paper, we study the problem of object counting with incomplete annotations.
A viewpoint invariant approach for crowd counting
Kong, D., Gray, D., Tao, H.: · 2006
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Face recognition using kernel ridge regression
An, S., Liu, W., Venkatesh, S.: · 2007
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Privacy preserving crowd monitoring: Counting people without people models or tracking
Chan, A.B., Liang, Z.J., Vasconcelos, N.: · 2008
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Learning to count objects in images
Lempitsky, V.S., Zisserman, A.: · 2010
Earlier work this paper cites.
Shape-based human detection and segmentation via hierarchical part-template matching
Lin, Z., Davis, L.S.: · 2010
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The pascal visual object classes (VOC) challenge
Everingham, M., Gool, L.J.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: · 2010
Earlier work this paper cites.
Density-aware person detection and tracking in crowds
Rodriguez, M., Laptev, I., Sivic, J., Audibert, J.: · 2011
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Feature mining for localised crowd counting
Chen, K., Loy, C.C., Gong, S., Xiang, T.: · 2012
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Cumulative attribute space for age and crowd density estimation
Chen, K., Gong, S., Xiang, T., Loy, C.C.: · 2013
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Interactive object counting
Arteta, C., Lempitsky, V.S., Noble, J.A., Zisserman, A.: · 2014
Cited alongside, same era.
Automatic car counting method for unmanned aerial vehicle images
Moranduzzo, T., Melgani, F.: · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
Cited alongside, same era.
Cross-scene crowd counting via deep convolutional neural networks
Zhang, C., Li, H., Wang, X., Yang, X.: · 2015
Cited alongside, same era.
Aerial car detection and urban understanding
SSD: single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: · 2016
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A large contextual dataset for classification, detection and counting of cars with deep learning
Mundhenk, T.N., Konjevod, G., Sakla, W.A., Boakye, K.: · 2016
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Weakly supervised deep detection networks
Bilen, H., Vedaldi, A.: · 2016
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Contextlocnet: Context-aware deep network models for weakly supervised localization
Kantorov, V., Oquab, M., Cho, M., Laptev, I.: · 2016
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Drone-based object counting by spatially regularized regional proposal network
Hsieh, M., Lin, Y., Hsu, W.H.: · 2017
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YOLO9000: better, faster, stronger
Redmon, J., Farhadi, A.: · 2017
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Kamenetsky, D., Sherrah, J.: · 2015
Cited alongside, same era.
Fast R-CNN
Girshick, R.B.: · 2015
Cited alongside, same era.
Faster R-CNN: towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R.B., Sun, J.: · 2015
Cited alongside, same era.
Weakly supervised object localization with multi-fold multiple instance learning
Cinbis, R.G., Verbeek, J.J., Schmid, C.: · 2017
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Multiple instance detection network with online instance classifier refinement
Tang, P., Wang, X., Bai, X., Liu, W.: · 2017
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