{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T07:38:36Z","timestamp":1767857916464,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":78,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61976250"],"award-info":[{"award-number":["61976250"]}]},{"name":"National Natural Science Foundation of China","award":["No. U1811463"],"award-info":[{"award-number":["No. U1811463"]}]},{"name":"National Natural Science Foundation of China","award":["61876045"],"award-info":[{"award-number":["61876045"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,12]]},"DOI":"10.1145\/3394171.3413938","type":"proceedings-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T12:26:18Z","timestamp":1602505578000},"page":"2645-2654","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":59,"title":["Efficient Crowd Counting via Structured Knowledge Transfer"],"prefix":"10.1145","author":[{"given":"Lingbo","family":"Liu","sequence":"first","affiliation":[{"name":"Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hefeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianshui","family":"Chen","sequence":"additional","affiliation":[{"name":"DarkMatter AI Research, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanbin","family":"Li","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Lin","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University &amp; DarkMatter AI Research, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"[n.d.]. https:\/\/en.wikipedia.org\/wiki\/Edge_computing.  [n.d.]. https:\/\/en.wikipedia.org\/wiki\/Edge_computing."},{"key":"e_1_3_2_2_2_1","unstructured":"Deepak Babu Sam Neeraj N Sajjan R Venkatesh Babu and Mukundhan Srinivasan. 2018. Divide and Grow: Capturing Huge Diversity in Crowd Images With Incrementally Growing CNN. In CVPR. 3618--3626.  Deepak Babu Sam Neeraj N Sajjan R Venkatesh Babu and Mukundhan Srinivasan. 2018. Divide and Grow: Capturing Huge Diversity in Crowd Images With Incrementally Growing CNN. In CVPR. 3618--3626."},{"key":"e_1_3_2_2_3_1","volume-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561","author":"Badrinarayanan Vijay","year":"2015"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Lokesh Boominathan Srinivas SS Kruthiventi and R Venkatesh Babu. 2016. Crowdnet: A deep convolutional network for dense crowd counting. In ACM MM. ACM 640--644.  Lokesh Boominathan Srinivas SS Kruthiventi and R Venkatesh Babu. 2016. Crowdnet: A deep convolutional network for dense crowd counting. In ACM MM. ACM 640--644.","DOI":"10.1145\/2964284.2967300"},{"key":"e_1_3_2_2_5_1","unstructured":"Zhaowei Cai Xiaodong He Jian Sun and Nuno Vasconcelos. 2017. Deep learning with low precision by half-wave gaussian quantization. In CVPR. 5918--5926.  Zhaowei Cai Xiaodong He Jian Sun and Nuno Vasconcelos. 2017. Deep learning with low precision by half-wave gaussian quantization. In CVPR. 5918--5926."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Xinkun Cao Zhipeng Wang Yanyun Zhao and Fei Su. 2018. Scale Aggregation Network for Accurate and Efficient Crowd Counting. In ECCV. 734--750.  Xinkun Cao Zhipeng Wang Yanyun Zhao and Fei Su. 2018. Scale Aggregation Network for Accurate and Efficient Crowd Counting. In ECCV. 734--750.","DOI":"10.1007\/978-3-030-01228-1_45"},{"key":"e_1_3_2_2_7_1","first-page":"3","article-title":"Feature Mining for Localised Crowd Counting","volume":"1","author":"Chen Ke","year":"2012","journal-title":"BMVC"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Tianshui Chen Liang Lin Wangmeng Zuo Xiaonan Luo and Lei Zhang. 2018a. Learning a wavelet-like auto-encoder to accelerate deep neural networks. In AAAI.  Tianshui Chen Liang Lin Wangmeng Zuo Xiaonan Luo and Lei Zhang. 2018a. Learning a wavelet-like auto-encoder to accelerate deep neural networks. In AAAI.","DOI":"10.1609\/aaai.v32i1.12282"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240523"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350898"},{"key":"e_1_3_2_2_11_1","unstructured":"Weina Ge and Robert T Collins. 2009. Marked point processes for crowd counting. In CVPR. IEEE 2913--2920.  Weina Ge and Robert T Collins. 2009. Marked point processes for crowd counting. In CVPR. IEEE 2913--2920."},{"key":"e_1_3_2_2_12_1","unstructured":"Yunchao Gong Liu Liu Ming Yang and Lubomir Bourdev. 2014. Compressing deep convolutional networks using vector quantization. arXiv preprint arXiv:1412.6115 (2014).  Yunchao Gong Liu Liu Ming Yang and Lubomir Bourdev. 2014. Compressing deep convolutional networks using vector quantization. arXiv preprint arXiv:1412.6115 (2014)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350881"},{"key":"e_1_3_2_2_14_1","unstructured":"Song Han Huizi Mao and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015).  Song Han Huizi Mao and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)."},{"key":"e_1_3_2_2_15_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00067"},{"key":"e_1_3_2_2_17_1","unstructured":"Yihui He Xiangyu Zhang and Jian Sun. 2017. Channel pruning for accelerating very deep neural networks. In ICCV. 1389--1397.  Yihui He Xiangyu Zhang and Jian Sun. 2017. Channel pruning for accelerating very deep neural networks. In ICCV. 1389--1397."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013779"},{"key":"e_1_3_2_2_19_1","unstructured":"Geoffrey E Hinton Oriol Vinyals and Jeffrey Dean. 2015. Distilling the Knowledge in a Neural Network. arXiv: Machine Learning (2015).  Geoffrey E Hinton Oriol Vinyals and Jeffrey Dean. 2015. Distilling the Knowledge in a Neural Network. arXiv: Machine Learning (2015)."},{"key":"e_1_3_2_2_20_1","unstructured":"Zehao Huang and Naiyan Wang. 2017. Like what you like: Knowledge distill via neuron selectivity transfer. arXiv preprint arXiv:1707.01219 (2017).  Zehao Huang and Naiyan Wang. 2017. Like what you like: Knowledge distill via neuron selectivity transfer. arXiv preprint arXiv:1707.01219 (2017)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Haroon Idrees Imran Saleemi Cody Seibert and Mubarak Shah. 2013. Multi-source multi-scale counting in extremely dense crowd images. In CVPR. 2547--2554.  Haroon Idrees Imran Saleemi Cody Seibert and Mubarak Shah. 2013. Multi-source multi-scale counting in extremely dense crowd images. In CVPR. 2547--2554.","DOI":"10.1109\/CVPR.2013.329"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Haroon Idrees Muhmmad Tayyab Kishan Athrey Dong Zhang Somaya Al-Maadeed Nasir Rajpoot and Mubarak Shah. 2018. Composition Loss for Counting Density Map Estimation and Localization in Dense Crowds. In ECCV.  Haroon Idrees Muhmmad Tayyab Kishan Athrey Dong Zhang Somaya Al-Maadeed Nasir Rajpoot and Mubarak Shah. 2018. Composition Loss for Counting Density Map Estimation and Localization in Dense Crowds. In ECCV.","DOI":"10.1007\/978-3-030-01216-8_33"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Benoit Jacob Skirmantas Kligys Bo Chen Menglong Zhu Matthew Tang Andrew Howard Hartwig Adam and Dmitry Kalenichenko. 2018. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In CVPR. 2704--2713.  Benoit Jacob Skirmantas Kligys Bo Chen Menglong Zhu Matthew Tang Andrew Howard Hartwig Adam and Dmitry Kalenichenko. 2018. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In CVPR. 2704--2713.","DOI":"10.1109\/CVPR.2018.00286"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Xiaolong Jiang Zehao Xiao Baochang Zhang Xiantong Zhen Xianbin Cao David Doermann and Ling Shao. 2019. Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks. In CVPR. 6133--6142.  Xiaolong Jiang Zehao Xiao Baochang Zhang Xiantong Zhen Xianbin Cao David Doermann and Ling Shao. 2019. Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks. In CVPR. 6133--6142.","DOI":"10.1109\/CVPR.2019.00629"},{"key":"e_1_3_2_2_25_1","unstructured":"Hao Li Asim Kadav Igor Durdanovic Hanan Samet and Hans Peter Graf. 2016. Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016).  Hao Li Asim Kadav Igor Durdanovic Hanan Samet and Hans Peter Graf. 2016. Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Min Li Zhaoxiang Zhang Kaiqi Huang and Tieniu Tan. 2008. Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In ICPR. IEEE 1--4.  Min Li Zhaoxiang Zhang Kaiqi Huang and Tieniu Tan. 2008. Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In ICPR. IEEE 1--4.","DOI":"10.1109\/ICPR.2008.4761705"},{"key":"e_1_3_2_2_27_1","first-page":"367","article-title":"Crowded Scene Analysis: A Survey","volume":"25","author":"Li Teng","year":"2015","journal-title":"T-CSVT"},{"key":"e_1_3_2_2_28_1","unstructured":"Yuhong Li Xiaofan Zhang and Deming Chen. 2018. CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes. In CVPR. 1091--1100.  Yuhong Li Xiaofan Zhang and Deming Chen. 2018. CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes. In CVPR. 1091--1100."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Dongze Lian Jing Li Jia Zheng Weixin Luo and Shenghua Gao. 2019. Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization. In CVPR. 1821--1830.  Dongze Lian Jing Li Jia Zheng Weixin Luo and Shenghua Gao. 2019. Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization. In CVPR. 1821--1830.","DOI":"10.1109\/CVPR.2019.00192"},{"key":"e_1_3_2_2_30_1","volume-title":"Decidenet: Counting varying density crowds through attention guided detection and density estimation. In CVPR. 5197--5206.","author":"Liu Jiang","year":"2018"},{"key":"e_1_3_2_2_31_1","unstructured":"Lingbo Liu Zhilin Qiu Guanbin Li Shufan Liu Wanli Ouyang and Liang Lin. 2019 c. Crowd Counting with Deep Structured Scale Integration Network. In ICCV. 1774--1783.  Lingbo Liu Zhilin Qiu Guanbin Li Shufan Liu Wanli Ouyang and Liang Lin. 2019 c. Crowd Counting with Deep Structured Scale Integration Network. In ICCV. 1774--1783."},{"key":"e_1_3_2_2_32_1","unstructured":"Lingbo Liu Hongjun Wang Guanbin Li Wanli Ouyang and Liang Lin. 2018 d. Crowd counting using deep recurrent spatial-aware network. In IJCAI.  Lingbo Liu Hongjun Wang Guanbin Li Wanli Ouyang and Liang Lin. 2018 d. Crowd counting using deep recurrent spatial-aware network. In IJCAI."},{"key":"e_1_3_2_2_33_1","unstructured":"Lingbo Liu Ruimao Zhang Jiefeng Peng Guanbin Li Bowen Du and Liang Lin. 2018 e. Attentive Crowd Flow Machines. In ACM MM. ACM 1553--1561.  Lingbo Liu Ruimao Zhang Jiefeng Peng Guanbin Li Bowen Du and Liang Lin. 2018 e. Attentive Crowd Flow Machines. In ACM MM. ACM 1553--1561."},{"key":"e_1_3_2_2_34_1","unstructured":"Lingbo Liu Jiajie Zhen Guanbin Li Geng Zhan Zhaocheng He Bowen Du and Liang Lin. 2020. Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems (2020).  Lingbo Liu Jiajie Zhen Guanbin Li Geng Zhan Zhaocheng He Bowen Du and Liang Lin. 2020. Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems (2020)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Ning Liu Yongchao Long Changqing Zou Qun Niu Li Pan and Hefeng Wu. 2019 b. ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding. In CVPR. 3225--3234.  Ning Liu Yongchao Long Changqing Zou Qun Niu Li Pan and Hefeng Wu. 2019 b. ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding. In CVPR. 3225--3234.","DOI":"10.1109\/CVPR.2019.00334"},{"key":"e_1_3_2_2_36_1","unstructured":"Weizhe Liu Mathieu Salzmann and Pascal Fua. 2019 d. Context-Aware Crowd Counting. In CVPR. 5099--5108.  Weizhe Liu Mathieu Salzmann and Pascal Fua. 2019 d. Context-Aware Crowd Counting. In CVPR. 5099--5108."},{"key":"e_1_3_2_2_37_1","unstructured":"Xingyu Liu Jeff Pool Song Han and William J Dally. 2018b. Efficient sparse-winograd convolutional neural networks. arXiv preprint arXiv:1802.06367 (2018).  Xingyu Liu Jeff Pool Song Han and William J Dally. 2018b. Efficient sparse-winograd convolutional neural networks. arXiv preprint arXiv:1802.06367 (2018)."},{"key":"e_1_3_2_2_38_1","unstructured":"Xialei Liu Joost van de Weijer and Andrew D Bagdanov. 2018c. Leveraging Unlabeled Data for Crowd Counting by Learning to Rank. In CVPR.  Xialei Liu Joost van de Weijer and Andrew D Bagdanov. 2018c. Leveraging Unlabeled Data for Crowd Counting by Learning to Rank. In CVPR."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00271"},{"key":"e_1_3_2_2_40_1","unstructured":"Zhiheng Ma Xing Wei Xiaopeng Hong and Yihong Gong. 2019. Bayesian loss for crowd count estimation with point supervision. In ICCV. 6142--6151.  Zhiheng Ma Xing Wei Xiaopeng Hong and Yihong Gong. 2019. Bayesian loss for crowd count estimation with point supervision. In ICCV. 6142--6151."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413642"},{"key":"e_1_3_2_2_42_1","unstructured":"Seyed-Iman Mirzadeh Mehrdad Farajtabar Ang Li and Hassan Ghasemzadeh. 2019. Improved knowledge distillation via teacher assistant: Bridging the gap between student and teacher. arXiv preprint arXiv:1902.03393 (2019).  Seyed-Iman Mirzadeh Mehrdad Farajtabar Ang Li and Hassan Ghasemzadeh. 2019. Improved knowledge distillation via teacher assistant: Bridging the gap between student and teacher. arXiv preprint arXiv:1902.03393 (2019)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"crossref","unstructured":"Daniel Onoro-Rubio and Roberto J L\u00f3pez-Sastre. 2016. Towards perspective-free object counting with deep learning. In ECCV. Springer 615--629.  Daniel Onoro-Rubio and Roberto J L\u00f3pez-Sastre. 2016. Towards perspective-free object counting with deep learning. In ECCV. Springer 615--629.","DOI":"10.1007\/978-3-319-46478-7_38"},{"key":"e_1_3_2_2_44_1","unstructured":"Zhilin Qiu Lingbo Liu Guanbin Li Qing Wang Nong Xiao and Liang Lin. 2019. Crowd counting via multi-view scale aggregation networks. In ICME. IEEE 1498--1503.  Zhilin Qiu Lingbo Liu Guanbin Li Qing Wang Nong Xiao and Liang Lin. 2019. Crowd counting via multi-view scale aggregation networks. In ICME. IEEE 1498--1503."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","unstructured":"Viresh Ranjan Hieu Le and Minh Hoai. 2018. Iterative Crowd Counting. In ECCV.  Viresh Ranjan Hieu Le and Minh Hoai. 2018. Iterative Crowd Counting. In ECCV.","DOI":"10.1007\/978-3-030-01234-2_17"},{"key":"e_1_3_2_2_46_1","unstructured":"Adriana Romero Nicolas Ballas Samira Ebrahimi Kahou Antoine Chassang Carlo Gatta and Yoshua Bengio. 2014. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014).  Adriana Romero Nicolas Ballas Samira Ebrahimi Kahou Antoine Chassang Carlo Gatta and Yoshua Bengio. 2014. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"D. Ryan S. Denman C. Fookes and S. Sridharan. 2009. Crowd Counting Using Multiple Local Features. In DICTA. 81--88.  D. Ryan S. Denman C. Fookes and S. Sridharan. 2009. Crowd Counting Using Multiple Local Features. In DICTA. 81--88.","DOI":"10.1109\/DICTA.2009.22"},{"key":"e_1_3_2_2_48_1","first-page":"6","article-title":"Switching convolutional neural network for crowd counting","volume":"1","author":"Sam Deepak Babu","year":"2017","journal-title":"CVPR"},{"key":"e_1_3_2_2_49_1","unstructured":"Bharat Bhusan Sau and Vineeth N Balasubramanian. 2016. Deep model compression: Distilling knowledge from noisy teachers. arXiv preprint arXiv:1610.09650 (2016).  Bharat Bhusan Sau and Vineeth N Balasubramanian. 2016. Deep model compression: Distilling knowledge from noisy teachers. arXiv preprint arXiv:1610.09650 (2016)."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"T Semertzidis K Dimitropoulos A Koutsia and N Grammalidis. 2010. Video sensor network for real-time traffic monitoring and surveillance. IET intelligent transport systems Vol. 4 2 (2010) 103--112.  T Semertzidis K Dimitropoulos A Koutsia and N Grammalidis. 2010. Video sensor network for real-time traffic monitoring and surveillance. IET intelligent transport systems Vol. 4 2 (2010) 103--112.","DOI":"10.1049\/iet-its.2008.0092"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Chong Shang Haizhou Ai and Bo Bai. 2016. End-to-end crowd counting via joint learning local and global count. In ICIP. IEEE 1215--1219.  Chong Shang Haizhou Ai and Bo Bai. 2016. End-to-end crowd counting via joint learning local and global count. In ICIP. IEEE 1215--1219.","DOI":"10.1109\/ICIP.2016.7532551"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"crossref","unstructured":"Zan Shen Yi Xu Bingbing Ni Minsi Wang Jianguo Hu and Xiaokang Yang. 2018. Crowd Counting via Adversarial Cross-Scale Consistency Pursuit. In CVPR. 5245--5254.  Zan Shen Yi Xu Bingbing Ni Minsi Wang Jianguo Hu and Xiaokang Yang. 2018. Crowd Counting via Adversarial Cross-Scale Consistency Pursuit. In CVPR. 5245--5254.","DOI":"10.1109\/CVPR.2018.00550"},{"key":"e_1_3_2_2_53_1","unstructured":"Miaojing Shi Zhaohui Yang Chao Xu and Qijun Chen. 2019 b. Revisiting perspective information for efficient crowd counting. In CVPR. 7279--7288.  Miaojing Shi Zhaohui Yang Chao Xu and Qijun Chen. 2019 b. Revisiting perspective information for efficient crowd counting. In CVPR. 7279--7288."},{"key":"e_1_3_2_2_54_1","unstructured":"Zenglin Shi Pascal Mettes and Cees GM Snoek. 2019 a. Counting with focus for free. In ICCV. 4200--4209.  Zenglin Shi Pascal Mettes and Cees GM Snoek. 2019 a. Counting with focus for free. In ICCV. 4200--4209."},{"key":"e_1_3_2_2_55_1","unstructured":"Zenglin Shi Le Zhang Yun Liu Xiaofeng Cao Yangdong Ye Ming-Ming Cheng and Guoyan Zheng. 2018. Crowd Counting With Deep Negative Correlation Learning. In CVPR. 5382--5390.  Zenglin Shi Le Zhang Yun Liu Xiaofeng Cao Yangdong Ye Ming-Ming Cheng and Guoyan Zheng. 2018. Crowd Counting With Deep Negative Correlation Learning. In CVPR. 5382--5390."},{"key":"e_1_3_2_2_56_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).  Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"crossref","unstructured":"Vishwanath A Sindagi and Vishal M Patel. 2017a. Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In AVSS. IEEE 1--6.  Vishwanath A Sindagi and Vishal M Patel. 2017a. Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In AVSS. IEEE 1--6.","DOI":"10.1109\/AVSS.2017.8078491"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.206"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"crossref","unstructured":"Vishwanath A Sindagi and Vishal M Patel. 2019. Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting. In ICCV. 1002--1012.  Vishwanath A Sindagi and Vishal M Patel. 2019. Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting. In ICCV. 1002--1012.","DOI":"10.1109\/ICCV.2019.00109"},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"crossref","unstructured":"Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke and Andrew Rabinovich. 2015. Going deeper with convolutions. In CVPR. 1--9.  Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke and Andrew Rabinovich. 2015. Going deeper with convolutions. In CVPR. 1--9.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_2_61_1","unstructured":"Cheng Tai Tong Xiao Yi Zhang Xiaogang Wang etal 2015. Convolutional neural networks with low-rank regularization. arXiv preprint arXiv:1511.06067 (2015).  Cheng Tai Tong Xiao Yi Zhang Xiaogang Wang et al. 2015. Convolutional neural networks with low-rank regularization. arXiv preprint arXiv:1511.06067 (2015)."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350914"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"crossref","unstructured":"Elad Walach and Lior Wolf. 2016. Learning to count with CNN boosting. In ECCV. Springer 660--676.  Elad Walach and Lior Wolf. 2016. Learning to count with CNN boosting. In ECCV. Springer 660--676.","DOI":"10.1007\/978-3-319-46475-6_41"},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"crossref","unstructured":"Feng Xiong Xingjian Shi and Dit-Yan Yeung. 2017. Spatiotemporal modeling for crowd counting in videos. In ICCV. IEEE.  Feng Xiong Xingjian Shi and Dit-Yan Yeung. 2017. Spatiotemporal modeling for crowd counting in videos. In ICCV. IEEE.","DOI":"10.1109\/ICCV.2017.551"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"crossref","unstructured":"Haipeng Xiong Hao Lu Chengxin Liu Liang Liu Zhiguo Cao and Chunhua Shen. 2019. From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer. In ICCV. 8362--8371.  Haipeng Xiong Hao Lu Chengxin Liu Liang Liu Zhiguo Cao and Chunhua Shen. 2019. From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer. In ICCV. 8362--8371.","DOI":"10.1109\/ICCV.2019.00845"},{"key":"e_1_3_2_2_66_1","unstructured":"Zhaoyi Yan Yuchen Yuan Wangmeng Zuo Xiao Tan Yezhen Wang Shilei Wen and Errui Ding. 2019. Perspective-Guided Convolution Networks for Crowd Counting. In ICCV. 952--961.  Zhaoyi Yan Yuchen Yuan Wangmeng Zuo Xiao Tan Yezhen Wang Shilei Wen and Errui Ding. 2019. Perspective-Guided Convolution Networks for Crowd Counting. In ICCV. 952--961."},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"crossref","unstructured":"Junho Yim Donggyu Joo Jihoon Bae and Junmo Kim. 2017. A gift from knowledge distillation: Fast optimization network minimization and transfer learning. In CVPR. 4133--4141.  Junho Yim Donggyu Joo Jihoon Bae and Junmo Kim. 2017. A gift from knowledge distillation: Fast optimization network minimization and transfer learning. In CVPR. 4133--4141.","DOI":"10.1109\/CVPR.2017.754"},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.05.042"},{"key":"e_1_3_2_2_69_1","unstructured":"Sergey Zagoruyko and Nikos Komodakis. 2016. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016).  Sergey Zagoruyko and Nikos Komodakis. 2016. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016)."},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-008-0132-4"},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"crossref","unstructured":"Anran Zhang Lei Yue Jiayi Shen Fan Zhu Xiantong Zhen Xianbin Cao and Ling Shao. 2019 a. Attentional Neural Fields for Crowd Counting. In ICCV. 5714--5723.  Anran Zhang Lei Yue Jiayi Shen Fan Zhu Xiantong Zhen Xianbin Cao and Ling Shao. 2019 a. Attentional Neural Fields for Crowd Counting. In ICCV. 5714--5723.","DOI":"10.1109\/ICCV.2019.00581"},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"crossref","unstructured":"Cong Zhang Hongsheng Li Xiaogang Wang and Xiaokang Yang. 2015. Cross-scene crowd counting via deep convolutional neural networks. In CVPR. 833--841.  Cong Zhang Hongsheng Li Xiaogang Wang and Xiaokang Yang. 2015. Cross-scene crowd counting via deep convolutional neural networks. In CVPR. 833--841.","DOI":"10.1109\/CVPR.2015.7298684"},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"crossref","unstructured":"Feng Zhang Xiatian Zhu and Mao Ye. 2019 b. Fast Human Pose Estimation. In CVPR.  Feng Zhang Xiatian Zhu and Mao Ye. 2019 b. Fast Human Pose Estimation. In CVPR.","DOI":"10.1109\/CVPR.2019.00363"},{"key":"e_1_3_2_2_74_1","volume-title":"Fcn-rlstm: Deep spatio-temporal neural networks for vehicle counting in city cameras","author":"Zhang Shanghang","year":"2017"},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"crossref","unstructured":"Ying Zhang Tao Xiang Timothy M Hospedales and Huchuan Lu. 2018. Deep mutual learning. In CVPR. 4320--4328.  Ying Zhang Tao Xiang Timothy M Hospedales and Huchuan Lu. 2018. Deep mutual learning. In CVPR. 4320--4328.","DOI":"10.1109\/CVPR.2018.00454"},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"crossref","unstructured":"Yingying Zhang Desen Zhou Siqin Chen Shenghua Gao and Yi Ma. 2016. Single-image crowd counting via multi-column convolutional neural network. In CVPR. 589--597.  Yingying Zhang Desen Zhou Siqin Chen Shenghua Gao and Yi Ma. 2016. Single-image crowd counting via multi-column convolutional neural network. In CVPR. 589--597.","DOI":"10.1109\/CVPR.2016.70"},{"key":"e_1_3_2_2_77_1","volume-title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160","author":"Zhou Shuchang","year":"2016"},{"key":"e_1_3_2_2_78_1","unstructured":"Michael Zhu and Suyog Gupta. 2017. To prune or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878 (2017).  Michael Zhu and Suyog Gupta. 2017. To prune or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878 (2017)."}],"event":{"name":"MM '20: The 28th ACM International Conference on Multimedia","location":"Seattle WA USA","acronym":"MM '20","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 28th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394171.3413938","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394171.3413938","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:32:07Z","timestamp":1750195927000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394171.3413938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":78,"alternative-id":["10.1145\/3394171.3413938","10.1145\/3394171"],"URL":"https:\/\/doi.org\/10.1145\/3394171.3413938","relation":{},"subject":[],"published":{"date-parts":[[2020,10,12]]},"assertion":[{"value":"2020-10-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}