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Current class-agnostic counting methods can generalise to unseen classes but usually require reference images to define the type of object to be counted, as well as instance annotations during training.
Estimation of crowd density using image processing
A. N. Marana, S. Velastin, L. Costa, and R. Lotufo · 1997
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Bayesian poisson regression for crowd counting
A. B. Chan and N. Vasconcelos · 2009
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Discriminative models for multi-class object layout
C. Desai, D. Ramanan, and C. C. Fowlkes · 2011
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Automatic adaptation of a generic pedestrian detector to a specific traffic scene
M. Wang and X. Wang · 2011
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On detection of multiple object instances using hough transforms
O. Barinova, V. Lempitsky, and P. Kholi · 2012
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Interactive object counting
C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Deep people counting in extremely dense crowds
C. Wang, H. Zhang, L. Yang, S. Liu, and X. Cao · 2015
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Cross-scene crowd counting via deep convolutional neural networks
C. Zhang, H. Li, X. Wang, and X. Yang · 2015
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Gaussian process density counting from weak supervision
M. v. Borstel, M. Kandemir, P. Schmidt, M. K. Rao, K. Rajamani, and F. A. Hamprecht · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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A large contextual dataset for classification, detection and counting of cars with deep learning
T. N. Mundhenk, G. Konjevod, W. A. Sakla, and K. Boakye · 2016
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You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
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Single-image crowd counting via multi-column convolutional neural network
Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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Drone-based object counting by spatially regularized regional proposal network
M.-R. Hsieh, Y.-L. Lin, and W. H. Hsu · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Scale aggregation network for accurate and efficient crowd counting
X. Cao, Z. Wang, Y. Zhao, and F. Su · 2018
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Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes
Y. Li, X. Zhang, and D. Chen · 2018
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Leveraging unlabeled data for crowd counting by learning to rank
X. Liu, J. Van De Weijer, and A. D. Bagdanov · 2018
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Class-agnostic counting
E. Lu, W. Xie, and A. Zisserman · 2018
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One-shot instance segmentation
C. Michaelis, I. Ustyuzhaninov, M. Bethge, and A. S. Ecker · 2018
Emerging properties in self-supervised vision transformers
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin · 2021
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Attention in crowd counting using the transformer and density map to improve counting result
P. T. Do · 2021
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Fine-grained multi-class object counting
H. Go, J. Byun, B. Park, M.-A. Choi, S. Yoo, and C. Kim · 2021
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Towards using count-level weak supervision for crowd counting
Y. Lei, Y. Liu, P. Zhang, and L. Liu · 2021
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Transcrowd: Weakly-supervised crowd counting with transformer
D. Liang, X. Chen, W. Xu, Y. Zhou, and X. Bai · 2021
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Microscopy cell counting and detection with fully convolutional regression networks
W. Xie, J. A. Noble, and A. Zisserman · 2018
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Few-shot object detection via feature reweighting
B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng, and T. Darrell · 2019
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Almost unsupervised learning for dense crowd counting
D. B. Sam, N. N. Sajjan, H. Maurya, and R. V. Babu · 2019
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Ha-ccn: Hierarchical attention-based crowd counting network
V. A. Sindagi and V. M. Patel · 2019
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Adaptive dilated network with self-correction supervision for counting
S. Bai, Z. He, Y. Qiao, H. Hu, W. Wu, and J. Yan · 2020
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End-to-end object detection with transformers
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko · 2020
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H. Lin, X. Hong, and Y. Wang · 2021
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Learning to count everything
V. Ranjan, U. Sharma, T. Nguyen, and M. Hoai · 2021
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Boosting crowd counting with transformers
G. Sun, Y. Liu, T. Probst, D. P. Paudel, N. Popovic, and L. Van Gool · 2021
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Training data-efficient image transformers & distillation through attention
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou · 2021
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Class-agnostic few-shot object counting
S.-D. Yang, H.-T. Su, W. H. Hsu, and W.-C. Chen · 2021
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An end-to-end transformer model for crowd localization
D. Liang, W. Xu, and X. Bai · 2022
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A convnet for the 2020s
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie · 2022
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Exemplar free class agnostic counting
V. Ranjan and M. Hoai · 2022
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Crowdformer: Weakly-supervised crowd counting with improved generalizability
S. S. Savner and V. Kanhangad · 2022
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Represent, compare, and learn: A similarity-aware framework for class-agnostic counting
M. Shi, H. Lu, C. Feng, C. Liu, and Z. Cao · 2022
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Joint cnn and transformer network via weakly supervised learning for efficient crowd counting
F. Wang, K. Liu, F. Long, N. Sang, X. Xia, and J. Sang · 2022
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