Erkun Yang

2.2k total citations
31 papers, 1.4k citations indexed

About

Erkun Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Erkun Yang has authored 31 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 5 papers in Cognitive Neuroscience. Recurrent topics in Erkun Yang's work include Advanced Image and Video Retrieval Techniques (13 papers), Multimodal Machine Learning Applications (9 papers) and Video Surveillance and Tracking Methods (8 papers). Erkun Yang is often cited by papers focused on Advanced Image and Video Retrieval Techniques (13 papers), Multimodal Machine Learning Applications (9 papers) and Video Surveillance and Tracking Methods (8 papers). Erkun Yang collaborates with scholars based in China, United States and Australia. Erkun Yang's co-authors include Cheng Deng, Dacheng Tao, Wei Liu, Tongliang Liu, Xinbo Gao, Xianglong Liu, Pew‐Thian Yap, Mingxia Liu, Dinggang Shen and Dongren Yao and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Transactions on Medical Imaging.

In The Last Decade

Erkun Yang

30 papers receiving 1.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Erkun Yang China 16 1.0k 476 144 105 48 31 1.4k
Hu Lu China 15 276 0.3× 169 0.4× 130 0.9× 44 0.4× 66 1.4× 52 668
Haitao Gan China 15 255 0.2× 354 0.7× 163 1.1× 37 0.4× 50 1.0× 77 746
Yann Lecun France 2 583 0.6× 602 1.3× 36 0.3× 44 0.4× 24 0.5× 5 959
Jian Pu China 13 383 0.4× 164 0.3× 49 0.3× 36 0.3× 85 1.8× 55 672
Zhaoqiang Xia China 19 977 0.9× 315 0.7× 64 0.4× 46 0.4× 127 2.6× 92 1.5k
Masakazu Matsugu Japan 8 278 0.3× 166 0.3× 63 0.4× 42 0.4× 37 0.8× 18 601
Mahdieh Soleymani Baghshah Iran 15 323 0.3× 423 0.9× 23 0.2× 59 0.6× 33 0.7× 52 676
Jzau‐Sheng Lin Taiwan 14 250 0.2× 190 0.4× 111 0.8× 30 0.3× 46 1.0× 42 590
Dimitrios Kollias United Kingdom 15 447 0.4× 252 0.5× 110 0.8× 129 1.2× 55 1.1× 40 1.0k

Countries citing papers authored by Erkun Yang

Since Specialization
Citations

This map shows the geographic impact of Erkun Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Erkun Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erkun Yang more than expected).

Fields of papers citing papers by Erkun Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Erkun Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Erkun Yang. The network helps show where Erkun Yang may publish in the future.

Co-authorship network of co-authors of Erkun Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Erkun Yang. A scholar is included among the top collaborators of Erkun Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Erkun Yang. Erkun Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Yang, Yuchen, et al.. (2024). Robust Noisy Correspondence Learning with Equivariant Similarity Consistency. 17700–17709. 5 indexed citations
3.
Yang, Erkun, et al.. (2024). CrossFormer: Cross-Modal Representation Learning via Heterogeneous Graph Transformer. ACM Transactions on Multimedia Computing Communications and Applications. 20(12). 1–21. 6 indexed citations
4.
Yang, Yuchen, et al.. (2024). Mitigating data imbalance and noise: A divergence-based approach with enhanced sample selection. Neurocomputing. 605. 128269–128269. 2 indexed citations
5.
Song, Peipei, Dan Guo, Xun Yang, et al.. (2023). Emotion-Prior Awareness Network for Emotional Video Captioning. 589–600. 16 indexed citations
6.
Yang, Erkun, Lihong Wang, Zhanhao Mo, et al.. (2023). Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model. Frontiers in Neuroscience. 17. 1209906–1209906. 2 indexed citations
7.
Yang, Shuo, Erkun Yang, Bo Han, et al.. (2023). A Parametrical Model for Instance-Dependent Label Noise. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12). 14055–14068. 6 indexed citations
8.
Yang, Erkun, et al.. (2023). Learning with Diversity: Self-Expanded Equalization for Better Generalized Deep Metric Learning. 19308–19317. 5 indexed citations
9.
Yang, Erkun, Dongren Yao, Tongliang Liu, & Cheng Deng. (2022). Mutual Quantization for Cross-Modal Search with Noisy Labels. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7541–7550. 29 indexed citations
10.
Ma, Lei, Daeseung Kim, Chunfeng Lian, et al.. (2021). Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning. Lecture notes in computer science. 12904. 459–468. 7 indexed citations
11.
Guan, Hao, Yunbi Liu, Erkun Yang, et al.. (2021). Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification. Medical Image Analysis. 71. 102076–102076. 85 indexed citations
12.
Yang, Erkun, Lihong Wang, David C. Steffens, Guy G. Potter, & Mingxia Liu. (2021). Deep Factor Regression For Computer-Aided Analysis of Major Depressive Disorders With Structural MRI Data. 2 indexed citations
13.
Yao, Dongren, Jing Sui, Erkun Yang, et al.. (2020). Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder Using Resting-State fMRI. Lecture notes in computer science. 12436. 1–10. 20 indexed citations
14.
Yang, Erkun, Dongren Yao, Bing Cao, et al.. (2020). Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search. Lecture notes in computer science. 12261. 191–201. 1 indexed citations
15.
Deng, Cheng, Erkun Yang, Tongliang Liu, et al.. (2019). Unsupervised Semantic-Preserving Adversarial Hashing for Image Search. IEEE Transactions on Image Processing. 28(8). 4032–4044. 138 indexed citations
16.
Deng, Cheng, Erkun Yang, Tongliang Liu, & Dacheng Tao. (2019). Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search. IEEE Transactions on Neural Networks and Learning Systems. 31(6). 2189–2201. 66 indexed citations
17.
Yang, Erkun, Cheng Deng, Tongliang Liu, Wei Liu, & Dacheng Tao. (2018). Semantic Structure-based Unsupervised Deep Hashing. 1064–1070. 120 indexed citations
18.
Yang, Erkun, Cheng Deng, Chao Li, et al.. (2018). Shared Predictive Cross-Modal Deep Quantization. IEEE Transactions on Neural Networks and Learning Systems. 29(11). 5292–5303. 131 indexed citations
19.
Yang, Erkun, Tongliang Liu, Cheng Deng, & Dacheng Tao. (2018). Adversarial Examples for Hamming Space Search. IEEE Transactions on Cybernetics. 50(4). 1473–1484. 81 indexed citations
20.
Wang, Hao, Yanhua Yang, Erkun Yang, & Cheng Deng. (2017). Exploring hybrid spatio-temporal convolutional networks for human action recognition. Multimedia Tools and Applications. 76(13). 15065–15081. 15 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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