research-article

Improving Crowd Density Estimation by Fusing Aerial Images and Radio Signals

Authors:

Hong-Han Shuai,

Wen-Huang ChengAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 3

Article No.: 84, Pages 1 - 23

https://doi.org/10.1145/3492346

Published: 04 March 2022 Publication History

Abstract

A recent line of research focuses on crowd density estimation from RGB images for a variety of applications, for example, surveillance and traffic flow control. The performance drops dramatically for low-quality images, such as occlusion, or poor light conditions. However, people are equipped with various wireless devices, allowing the received signals to be easily collected at the base station. As such, another line of research utilizes received signals for crowd counting. Nevertheless, received signals offer only information regarding the number of people, while an accurate density map cannot be derived. As unmanned aerial vehicles (UAVs) are now treated as flying base stations and equipped with cameras, we make the first attempt to leverage both RGB images and received signals for crowd density estimation on UAVs. Specifically, we propose a novel network to effectively fuse the RGB images and received signal strength (RSS) information. Moreover, we design a new loss function that considers the uncertainty from RSS and makes the prediction consistent with the received signals. Experimental results show that the proposed method successfully helps break the limit of traditional crowd density estimation methods and achieves state-of-the-art performance. The proposed dataset is released as a public download for future research.

References

[1]

Jeffrey G. Andrews, Stefano Buzzi, Wan Choi, Stephen Hanly, Angel Lozano, Anthony C. K. Soong, and Jianzhong Charlie Zhang. 2014. What will 5G be? IEEE Journal on Selected Areas in Communications 32, 6 (2014), 1065–1082.

[2]

Anas Basalamah. 2016. Automatic Update of Crowd and Traffic Data Using Device Monitoring. (Jul 2016). US Patent 9,401,086.

[3]

Jack Bresenham. 1965. Algorithm for computer control of a digital plotter. IBM Systems Journal 4, 1 (1965), 25–30.

Digital Library

[4]

Xinkun Cao, Zhipeng Wang, Yanyun Zhao, and Fei Su. 2020. Scale aggregation network for accurate and efficient crowd counting. In Proceedings of the European Conference on Computer Vision (ECCV’18), Munich, Germany. Springer, 757–773.

[5]

Antoni B. Chan and Nuno Vasconcelos. 2012. Counting people with low-level features and Bayesian regression. IEEE Transactions on Image Processing 21, 4 (April 2012), 2160–2177.

Digital Library

[6]

Jiwei Chen, Wen Su, and Zengfu Wang. 2020. Crowd counting with crowd attention convolutional neural network. Neurocomputing 382 (2020), 210–220. DOI:

Digital Library

[7]

Zhi-Qi Cheng, Jun-Xiu Li, Qi Dai, Xiao Wu, and Alexander G. Hauptmann. 2019. Learning spatial awareness to improve crowd counting. In Proceedings of the IEEE Conference on Computer Vision (ICCV’19), Seoul, Korea (South). 6151–6160.

[8]

Zhi-Qi Cheng, Jun-Xiu Li, Qi Dai, Xiao Wu, Jun-Yan He, and Alexander G. Hauptmann. 2019. Improving the learning of multi-column convolutional neural network for crowd counting. In Proceedings of the 27th ACM International Conference on Multimedia (MM’19) Nice, France. Association for Computing Machinery, 1897–1906.

Digital Library

[9]

Navneet Dalal and Bill Triggs. 2005. Histograms of oriented gradients for human detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, Vol. 1. 886–893.

Digital Library

[10]

Simone Di Domenico, Mauro De Sanctis, Ernestina Cianca, and Giuseppe Bianchi. 2016. A trained-once crowd counting method using differential WiFi channel state information. In Proceedings of the 3rd International Workshop on Physical Analytics (WPA’16), Singapore. 37–42.

Digital Library

[11]

Piotr Dollár, Christian Wojek, Bernt Schiele, and Pietro Perona. 2012. Pedestrian detection: An evaluation of the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 4 (April 2012), 743–761.

Digital Library

[12]

Huiyuan Fu, Huadong Ma, and Hongtian Xiao. 2014. Crowd counting via head detection and motion flow estimation. In Proceedings of the 22nd ACM International Conference on Multimedia (MM’14) Orlando, FL, USA. ACM, 877–880.

Digital Library

[13]

Junyu Gao, Qi Wang, and Yuan Yuan. 2019. SCAR: Spatial-/channel-wise attention regression networks for crowd counting. Neurocomputing 363 (October 2019), 1–8.

Digital Library

[14]

Dan Guo, Kun Li, Zheng-Jun Zha, and Meng Wang. 2019. DADNet: Dilated-attention-deformable ConvNet for crowd counting. In Proceedings of the 27th ACM International Conference on Multimedia (MM’19) Nice, France. ACM, 1823–1832.

Digital Library

[15]

Marcus Handte, Muhammad Umer Iqbal, Stephan Wagner, Wolfgang Apolinarski, Pedro Marrón, Eva Maria Muñoz Navarro, Santiago Martinez, Sara Izquierdo Barthelemy, and Mario G. Fernández. 2014. Crowd density estimation for public transport vehicles. In Proceedings of the International Conference on Extending Database Technology/International Conference on Database Theory (EDBT/ICDT’14), Joint Conference, Athens, Greece. CEUR-WS.org, 315–322.

[16]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2014. Spatial pyramid pooling in deep convolutional networks for visual recognition. In Proceedings of the European Conference on Computer Vision (ECCV’14), Zurich, Switzerland. Springer, 346–361.

[17]

Yaocong Hu, Huan Chang, Fudong Nian, Yan Wang, and Teng Li. 2016. Dense crowd counting from still images with convolutional neural networks. Journal of Visual Communication and Image Representation 38 (2016), 530–539. DOI:

Digital Library

[18]

Haroon Idrees, Imran Saleemi, Cody Seibert, and Mubarak Shah. 2013. Multi-source multi-scale counting in extremely dense crowd images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13), Portland, OR, USA. IEEE Computer Society, 2547–2554.

Digital Library

[19]

Haroon Idrees, Khurram Soomro, and Mubarak Shah. 2015. Detecting humans in dense crowds using locally-consistent scale prior and global occlusion reasoning. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 10 (2015), 1986–1998.

Digital Library

[20]

X. Jiang, L. Zhang, M. Xu, T. Zhang, P. Lv, B. Zhou, X. Yang, and Y. Pang. 2020. Attention scaling for crowd counting. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20), Seattle, WA, USA. Computer Vision Foundation, 4705–4714.

[21]

Wahab Khawaja, Ismail Guvenc, David Matolak, Uwe-Carsten Fiebig, and Nicolas Schneckenburger. 2019. A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles. IEEE Communications Surveys Tutorials 21, 3 (2019), 2361–2391.

[22]

Mehmet Kemal Kocamaz, Jian Gong, and Bernardo R. Pires. 2016. Vision-based counting of pedestrians and cyclists. In IEEE Winter Conference on Applications of Computer Vision (WACV’16). IEEE Computer Society, 1–8. DOI:

[23]

C. Lai, L. Wang, and Z. Han. 2019. Data-driven 3D placement of UAV base stations for arbitrarily distributed crowds. In 2019 IEEE Global Communications Conference (GLOBECOM’19) Waikoloa, HI, USA. IEEE, 1–6. DOI:

Digital Library

[24]

Wei-Cheng Lai, Zi-Xiang Xia, Hao-Siang Lin, Lien-Feng Hsu, Hong-Han Shuai, I-Hong Jhuo, and Wen-Huang Cheng. 2020. Trajectory prediction in heterogeneous environment via attended ecology embedding. In Proceedings of the ACM International Conference on Multimedia Virtual Event/Seattle, WA, USA. ACM, 202–210.

Digital Library

[25]

Teng Li, Huan Chang, Meng Wang, Bingbing Ni, Richang Hong, and Shuicheng Yan. 2015. Crowded scene analysis: A survey. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT’15) 25, 3 (2015), 367–386. DOI:

Digital Library

[26]

Yuhong Li, Xiaofan Zhang, and Deming Chen. 2018. CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18) Salt Lake City, UT, USA. IEEE Computer Society, 1091–1100.

[27]

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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19), Long Beach, CA, USA. Computer Vision Foundation, 1821–1830.

[28]

Chuanbin Liu, Hongtao Xie, Zhengjun Zha, Lingyun Yu, Zhineng Chen, and Yongdong Zhang. 2020. Bidirectional attention-recognition model for fine-grained object classification. IEEE Transactions on Multimedia 22, 7 (2020), 1785–1795. DOI:

[29]

Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, and Arnt-Børre Salberg. 2019. Dense dilated convolutions merging network for semantic mapping of remote sensing images. In Proceedings of Joint Urban Remote Sensing Event (JURSE’19) Vannes, France. IEEE, 1–4.

[30]

Weizhe Liu, Krzysztof Maciej Lis, Mathieu Salzmann, and Pascal Fua. 2019. Geometric and physical constraints for drone-based head plane crowd density estimation. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’19), Macau, SAR, China. IEEE, 244–249.

[31]

Weizhe Liu, Mathieu Salzmann, and Pascal Fua. 2019. Context-aware crowd counting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), Long Beach, CA, USA. Computer Vision Foundation, 5094–5103.

[32]

Xiyang Liu, Jie Yang, and Wenrui Ding. 2020. Adaptive mixture regression network with local counting map for crowd counting. In Proceedings of the European Conference on Computer Vision (ECCV’20), Glasgow, UK. Springer, 241–257.

Digital Library

[33]

Yan Liu, Lingqiao Liu, Peng Wang, Pingping Zhang, and Yinjie Lei. 2020. Semi-supervised crowd counting via self-training on surrogate tasks. In Proceedings of the European Conference on Computer Vision (ECCV’20), Glasgow, UK. Springer, 242–259.

Digital Library

[34]

Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully Convolutional Networks for Semantic Segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), Boston, MA, USA. IEEE Computer Society, 3431–3440.

[35]

Yu-Jen Ma, Hong-Han Shuai, and Wen-Huang Cheng. 2021. Spatiotemporal dilated convolution with uncertain matching for video-based crowd estimation. IEEE Transactions on Multimedia (2021), 1–1. DOI:

Digital Library

[36]

Zheng Ma and Antoni B. Chan. 2013. Crossing the line: Crowd counting by integer programming with local features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13), Portland, OR, USA. IEEE Computer Society, 2539–2546.

Digital Library

[37]

Yaik Ooi, Kong Zan Wai, Ian Tan, and Ooi Boon Sheng. 2016. Measuring the accuracy of crowd counting using WiFi probe-request-frame counting technique. Journal of Telecommunication, Electronic and Computer Engineering 8, 2 (2016), 79–81.

[38]

Xingang Pan, Jianping Shi, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2017. Spatial As Deep: Spatial CNN for Traffic Scene Understanding. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’18), New Orleans, Louisiana, USA. AAAI Press, 7276–7283.

[39]

David Ryan, Simon Denman, Clinton Fookes, and Sridha Sridharan. 2009. Crowd counting using multiple local features. In Proceedings of the Digital Image Computing: Techniques and Applications (DICTA’09) Melbourne, Australia. IEEE Computer Society, 81–88.

Digital Library

[40]

Deepak Babu Sam, Shiv Surya, and R. Venkatesh Babu. 2017. Switching convolutional neural network for crowd counting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17) Honolulu, HI, USA. IEEE Computer Society, 4031–4039.

[41]

Miaojing Shi, Zhaohui Yang, Chao Xu, and Qijun Chen. 2019. Revisiting perspective information for efficient crowd counting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19). Long Beach, CA, USA. Computer Vision Foundation, 7279–7288.

[42]

Kyosuke Shibata and Hiroshi Yamamoto. 2019. People crowd density estimation system using deep learning for radio wave sensing of cellular communication. In Proceedings of the International Conference on Artificial Intelligence in Information and Communication (ICAIIC’19) Okinawa, Japan. IEEE, 143–148.

[43]

Vishwanath A. Sindagi, Rajeev Yasarla, Deepak Sam Babu, R. Venkatesh Babu, and Vishal M. Patel. 2020. Learning to count in the crowd from limited labeled data. In Proceedings of the European Conference on Computer Vision (ECCV’20), Glasgow, UK. Springer, 212–229.

Digital Library

[44]

Chon Hou Sio, Yu-Jen Ma, Hong-Han Shuai, Jun-Cheng Chen, and Wen-Huang Cheng. 2020. S2SiamFC: Self-supervised fully convolutional Siamese network for visual tracking. In Proceedings of the ACM International Conference on Multimedia, Virtual Event / Seattle, WA, USA. ACM, 1948–1957.

Digital Library

[45]

Russell Stewart, Mykhaylo Andriluka, and Andrew Yan-Tak Ng. 2016. End-to-end people detection in crowded scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), Las Vegas, NV, USA. IEEE Computer Society, 2325–2333.

[46]

Gordon L. Stüber. 2017. Principles of Mobile Communication (4th ed.). Springer, Cham.

[47]

Xin Tan, Chun Tao, Tongwei Ren, Jinhui Tang, and Gangshan Wu. 2019. Crowd counting via multi-layer regression. In Proceedings of the 27th ACM International Conference on Multimedia (MM’19), Nice, France. ACM, 1907–1915.

Digital Library

[48]

Yukun Tian, Yiming Lei, Junping Zhang, and James Ze Wang. 2019. PaDNet: Pan-density crowd counting. IEEE Transactions on Image Processing 29 (November 2019), 2714–2727.

[49]

Haijun Wang, Haitao Zhao, Weiyu Wu, Jun Xiong, Dongtang Ma, and Jibo Wei. 2019. Deployment algorithms of flying base stations: 5G and beyond with UAVs. In IEEE Internet of Things Journal 6, 6 (2019), 10009–10027.

[50]

Qi Wang, Junyu Gao, Wei Lin, and Yuan Yuan. 2019. Learning from synthetic data for crowd counting in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19), Long Beach, CA, USA. Computer Vision Foundation, 8198–8207.

[51]

Shuheng Wang, Hanli Wang, and Qinyu Li. 2019. Multi-dilation network for crowd counting. In Proceedings of the ACM Multimedia Asia (MMAsia’19) Beijing, China. Association for Computing Machinery, Article 56, 1–6.

Digital Library

[52]

Zi-Xiang Xia, Wei-Cheng Lai, Li-Wu Tsao, Lien-Feng Hsu, Chih-Chia Hu Yu, Hong-Han Shuai, and Wen-Huang Cheng. 2020. Human-like traffic scene understanding system: A survey. IEEE Industrial Electronics Magazine 15, 1 (2020), 6–15.

[53]

Peng Yu, Wenjing Li, Fanqin Zhou, Lei Feng, Mengjun Yin, Shaoyong Guo, Zhipeng Gao, and Xuesong Qiu. 2018. Capacity enhancement for 5G networks using MmWave aerial base stations: Self-organizing architecture and approach. IEEE Wireless Communications 25, 4 (August 2018), 58–64.

[54]

Anran Zhang, Xiaolong Jiang, and Xianbin Cao Baochang Zhang. 2020. Multi-scale supervised attentive encoder-decoder network for crowd counting. ACM Transactions on Multimedia Computing, Communications, and Applications Article 28, 16, 1 (April 2020).

[55]

H. Zhang, L. Song, and Z. Han. 2020. Unmanned Aerial Vehicle Applications Over Cellular Networks for 5G and Beyond. Springer.

[56]

Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, and Yi Ma. 2016. Single-image crowd counting via multi-column convolutional neural network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16) Las Vegas, NV, USA. IEEE, 589–597.

[57]

Zhaoxiang Zhang, Mo Wang, and Xin Geng. 2015. Crowd counting in public video surveillance by label distribution learning. Neurocomputing 166 (Oct. 2015), 151–163.

Digital Library

[58]

Rui Zhou, Xiang Lu, Yang Fu, and Mingjie Tang. 2020. Device-free crowd counting with WiFi channel state information and deep neural networks. Wireless Networks 26, 5 (2020), 3495–3506. DOI:

Digital Library

[59]

Pengfei Zhu, Longyin Wen, Dawei Du, Xiao Bian, Qinghua Hu, and Haibin Ling. 2020. Vision Meets Drones: Past, Present and Future. (2020). arxiv:2001.06303

Cited By

Shu CChen YTan CLuo YDou H(2024)Enhancing trust transfer in supply chain finance: a blockchain-based transitive trust modelJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00557-w13:1Online publication date: 2-Jan-2024
https://dl.acm.org/doi/10.1186/s13677-023-00557-w
Guo ZHe XYang YQing LChen H(2024)DAG-YOLO: A Context-feature Adaptive Fusion Rotating Detection Network in Remote Sensing ImagesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3674978Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3674978
Qiu HLi HWu QShi HWang LMeng FXu L(2024)Learning Offset Probability Distribution for Accurate Object DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363721420:5(1-24)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1145/3637214
Show More Cited By

Index Terms

Improving Crowd Density Estimation by Fusing Aerial Images and Radio Signals
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

Recommendations

An evaluative review of the VTOL technologies for unmanned and manned aerial vehicles
Abstract
VTOL (Vertical Take-Off and Landing) capabilities are desired features of both UAVs (Unmanned Aerial Vehicles) and MAVs (Manned Aerial Vehicles) on condition that a comparable flight performance is achieved. VTOL is not only a very ...
Integral Backstepping Control of an Unconventional Dual-Fan Unmanned Aerial Vehicle

In this paper, a dynamic model of vertical take-off and landing (VTOL) aerial vehicles, having lateral and longitudinal rotor tilting mechanism, is first developed using a Newton---Euler formulation. Then an integral backstepping (IB) control technique ...
Unmanned aerial vehicles

As unmanned aerial vehicles have become more affordable, their popularity with the general public and commercial organisations has seen significant growth in recent years. Whilst remaining a device for both the hobbyist and aircraft-enthusiast to enjoy, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 3

August 2022

478 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3505208

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2022

Accepted: 01 October 2021

Revised: 01 September 2021

Received: 01 February 2021

Published in TOMM Volume 18, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

Ministry of Science and Technology (MOST) of Taiwan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
623
Total Downloads

Downloads (Last 12 months)81
Downloads (Last 6 weeks)8

Reflects downloads up to 26 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Shu CChen YTan CLuo YDou H(2024)Enhancing trust transfer in supply chain finance: a blockchain-based transitive trust modelJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00557-w13:1Online publication date: 2-Jan-2024
https://dl.acm.org/doi/10.1186/s13677-023-00557-w
Guo ZHe XYang YQing LChen H(2024)DAG-YOLO: A Context-feature Adaptive Fusion Rotating Detection Network in Remote Sensing ImagesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3674978Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3674978
Qiu HLi HWu QShi HWang LMeng FXu L(2024)Learning Offset Probability Distribution for Accurate Object DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363721420:5(1-24)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1145/3637214
Lin CHu X(2024)Efficient crowd density estimation with edge intelligence via structural reparameterization and knowledge transferApplied Soft Computing10.1016/j.asoc.2024.111366154:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111366
Sai VKim HFong BSai VKim HFong B(2024)Introduction6G Enabled Healthcare Systems10.1007/978-3-031-73849-4_1(1-12)Online publication date: 13-Nov-2024
https://doi.org/10.1007/978-3-031-73849-4_1
Xie YGuan L(2023)Sparsity-guided Discriminative Feature Encoding for Robust Keypoint DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362843220:3(1-22)Online publication date: 17-Oct-2023
https://dl.acm.org/doi/10.1145/3628432
Shi YYang SYang WShi DLi X(2023)Boosting Few-shot Object Detection with Discriminative Representation and Class MarginACM Transactions on Multimedia Computing, Communications, and Applications10.1145/360847820:3(1-19)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3608478
Yang DZhou YHong XZhang AWei XZeng LQiao ZWang WEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Pseudo Object Replay and Mining for Incremental Object DetectionProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611952(153-162)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611952
Liu HYan ZLiu BZhao JZhou YEl Saddik A(2023)Distilled Meta-learning for Multi-Class Incremental LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357604519:4(1-16)Online publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1145/3576045
Li ZXu PChang XYang LZhang YYao LChen X(2023)When Object Detection Meets Knowledge Distillation: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.325754645:8(10555-10579)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TPAMI.2023.3257546
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents