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In this paper, we propose a simple and effective way to improve one-look regression models for object counting from images.
Computational framework for simulating fluorescence microscope images with cell populations
Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O.: · 2007
Earlier work this paper cites.
Learning to count objects in images
Lempitsky, V., Zisserman, A.: · 2010
Earlier work this paper cites.
Learning to count with regression forest and structured labels
Fiaschi, L., Koethe, U., Nair, R., Hamprecht, F.A.: · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Feature mining for localised crowd counting
Chen, K., Loy, C.C., Gong, S., Xiang, T.: · 2012
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Lin, M., Chen, Q., Yan, S.: · 2013
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Interactive object counting
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
Earlier work this paper cites.
Cross-scene crowd counting via deep convolutional neural networks
Zhang, C., Li, H., Wang, X., Yang, X.: · 2015
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Learning to count with deep object features
Seguí, S., Pujol, O., Vitrià, J.: · 2015
Earlier work this paper cites.
Beyond classification: Structured regression for robust cell detection using convolutional neural network
Xie, Y., Xing, F., Kong, X., Su, H., Yang, L.: · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Deep people counting in extremely dense crowds
Wang, C., Zhang, H., Yang, L., Liu, S., Cao, X.: · 2015
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Fast r-cnn
Girshick, R.: · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Expert Syst. Appl. 42
PKLot - A Robust Dataset for Parking Lot Classification · 2015
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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
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2016
Later among the works it cites.
Mask r-cnn
He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
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Drone-based object counting by spatially regularized regional proposal network
Hsieh, M.R., Lin, Y.L., Hsu, W.H.: · 2017
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Deep plant phenomics: A deep learning platform for complex plant phenotyping tasks
Ubbens, J.R., Stavness, I.: · 2017
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Count-ception: Counting by fully convolutional redundant counting
Cohen, J.P., Boucher, G., Glastonbury, C.A., Lo, H.Z., Bengio, Y.: · 2017
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End-to-end instance segmentation with recurrent attention
Ren, M., Zemel, R.S.: · 2017
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Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: · 2016
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Microscopy cell counting and detection with fully convolutional regression networks
Xie, W., Noble, J.A., Zisserman, A.: · 2016
Cited alongside, same era.
Counting in the wild
Arteta, C., Lempitsky, V., Zisserman, A.: · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: · 2016
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Recurrent instance segmentation
Romera-Paredes, B., Torr, P.H.S.: · 2016
Cited alongside, same era.
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PyTorch
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A survey of recent advances in cnn-based single image crowd counting and density estimation
Sindagi, V.A., Patel, V.M.: · 2017
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