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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

DeepGCNs: Can GCNs Go As Deep As CNNs?
Guohao Li, Matthias Müller, Ali Thabet, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Open Access | Times Cited: 1113

Showing 1-25 of 1113 citing articles:

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Qingyong Hu, Bo Yang, Linhai Xie, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Open Access | Times Cited: 1523
Point Transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
Closed Access | Times Cited: 1248
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), pp. 14412-14420
Open Access | Times Cited: 657
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Yu Rong, Wenbing Huang, Tingyang Xu, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 544
G-TAD: Sub-Graph Localization for Temporal Action Detection
Mengmeng Xu, Chen Zhao, David S. Rojas, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Open Access | Times Cited: 404
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, et al.
(2021), pp. 1548-1554
Open Access | Times Cited: 391
An End-to-End Transformer Model for 3D Object Detection
Ishan Misra, Rohit Girdhar, Armand Joulin
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2021), pp. 2886-2897
Open Access | Times Cited: 333
The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
Tianfu Li, Zheng Zhou, Sinan Li, et al.
Mechanical Systems and Signal Processing (2021) Vol. 168, pp. 108653-108653
Closed Access | Times Cited: 321
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Richard J. Chen, Chengkuan Chen, Yicong Li, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 16123-16134
Open Access | Times Cited: 279
Data Augmentation for Graph Neural Networks
Tong Zhao, Yozen Liu, Leonardo Neves, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2021) Vol. 35, Iss. 12, pp. 11015-11023
Open Access | Times Cited: 250
Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision
Peng Wu, Jing Liu, Yujia Shi, et al.
Lecture notes in computer science (2020), pp. 322-339
Closed Access | Times Cited: 243
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
Joakim Skarding, Bogdan Gabryś, Katarzyna Musiał
IEEE Access (2021) Vol. 9, pp. 79143-79168
Open Access | Times Cited: 209
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
Sergi Abadal, Akshay Jain, Robert Guirado, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 9, pp. 1-38
Open Access | Times Cited: 200
Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy
Duarte Fernandes, António Silva, Rafael Névoa, et al.
Information Fusion (2020) Vol. 68, pp. 161-191
Open Access | Times Cited: 195
SGAS: Sequential Greedy Architecture Search
Guohao Li, Guocheng Qian, Itzel C. Delgadillo, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), pp. 1617-1627
Open Access | Times Cited: 185
Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit
Jinlei Zhang, Chen Feng, Zhiyong Cui, et al.
IEEE Transactions on Intelligent Transportation Systems (2020) Vol. 22, Iss. 11, pp. 7004-7014
Open Access | Times Cited: 173
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
Guocheng Qian, Abdulellah Abualshour, Guohao Li, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), pp. 11678-11687
Open Access | Times Cited: 172
3D Point Cloud Processing and Learning for Autonomous Driving: Impacting Map Creation, Localization, and Perception
Siheng Chen, Baoan Liu, Chen Feng, et al.
IEEE Signal Processing Magazine (2020) Vol. 38, Iss. 1, pp. 68-86
Closed Access | Times Cited: 171
SIGN: Scalable Inception Graph Neural Networks
Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 164
DeepGCNs: Making GCNs Go as Deep as CNNs
Guohao Li, Matthias Mueller, Guocheng Qian, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Vol. 45, Iss. 6, pp. 6923-6939
Open Access | Times Cited: 160
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
David Ahmedt‐Aristizabal, Mohammad Ali Armin, Simon Denman, et al.
Sensors (2021) Vol. 21, Iss. 14, pp. 4758-4758
Open Access | Times Cited: 160
FPConv: Learning Local Flattening for Point Convolution
Yiqun Lin, Zizheng Yan, Haibin Huang, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), pp. 4292-4301
Open Access | Times Cited: 155
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation
Jingwen Hu, Yuchen Liu, Jinming Zhao, et al.
(2021)
Open Access | Times Cited: 153
Graph Neural Networks with Heterophily
Jiong Zhu, Ryan A. Rossi, Anup Rao, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2021) Vol. 35, Iss. 12, pp. 11168-11176
Open Access | Times Cited: 151
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
Kenta Oono, Taiji Suzuki
arXiv (Cornell University) (2019)
Open Access | Times Cited: 149
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