Dong Gong

4.6k total citations · 1 hit paper
43 papers, 2.0k citations indexed

About

Dong Gong is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Dong Gong has authored 43 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 11 papers in Media Technology. Recurrent topics in Dong Gong's work include Advanced Image Processing Techniques (20 papers), Image and Signal Denoising Methods (11 papers) and Advanced Vision and Imaging (11 papers). Dong Gong is often cited by papers focused on Advanced Image Processing Techniques (20 papers), Image and Signal Denoising Methods (11 papers) and Advanced Vision and Imaging (11 papers). Dong Gong collaborates with scholars based in China, Australia and Russia. Dong Gong's co-authors include Anton van den Hengel, Lingqiao Liu, Moussa Reda Mansour, Svetha Venkatesh, Vuong Le, Budhaditya Saha, Yanning Zhang, Qinfeng Shi, Qingsen Yan and Chunhua Shen and has published in prestigious journals such as IEEE Transactions on Image Processing, Pattern Recognition and International Journal of Computer Vision.

In The Last Decade

Dong Gong

40 papers receiving 2.0k citations

Hit Papers

Memorizing Normality to Detect Anomaly: Memory-Augmented ... 2019 2026 2021 2023 2019 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection
    2019 1.0k citations Dong Gong, Lingqiao Liu et al. profile →

Peers

Dong Gong
Bumsub Ham South Korea
Paul Bergmann Germany
Michael Fauser Germany
David Sattlegger Germany
Lixin Zheng China
Qianru Sun Singapore
Moussa Reda Mansour Brazil
Weixin Luo China
Xu Jia China
Bumsub Ham South Korea
Dong Gong
Citations per year, relative to Dong Gong Dong Gong (= 1×) peers Bumsub Ham

Countries citing papers authored by Dong Gong

Since Specialization
Citations

This map shows the geographic impact of Dong Gong'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 Dong Gong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dong Gong more than expected).

Fields of papers citing papers by Dong Gong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dong Gong. 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 Dong Gong. The network helps show where Dong Gong may publish in the future.

Co-authorship network of co-authors of Dong Gong

This figure shows the co-authorship network connecting the top 25 collaborators of Dong Gong. A scholar is included among the top collaborators of Dong Gong 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 Dong Gong. Dong Gong 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.
Gong, Dong, et al.. (2025). Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning. 10087–10098. 2 indexed citations
2.
Ma, Hailan, et al.. (2025). Learning Informative Latent Representation for Quantum State Tomography. IEEE Transactions on Emerging Topics in Computational Intelligence. 1–11. 2 indexed citations
3.
Cheng, De, et al.. (2024). Continual All-in-One Adverse Weather Removal With Knowledge Replay on a Unified Network Structure. IEEE Transactions on Multimedia. 26. 8184–8196. 22 indexed citations
4.
Wang, Junyan, et al.. (2024). EGGen: Image Generation with Multi-entity Prior Learning through Entity Guidance. ANU Open Research (Australian National University). 6637–6645.
5.
Yin, Wei, et al.. (2024). SDGE: Stereo Guided Depth Estimation for 360°Camera Sets. 11179–11186. 1 indexed citations
6.
Yan, Qingsen, et al.. (2023). SharpFormer: Learning Local Feature Preserving Global Representations for Image Deblurring. IEEE Transactions on Image Processing. 32. 2857–2866. 21 indexed citations
7.
Yan, Qingsen, Julian Taylor, Dong Gong, et al.. (2023). Improved genomic prediction using machine learning with Variational Bayesian sparsity. Plant Methods. 19(1). 96–96. 4 indexed citations
8.
Li, Rui, Dong Gong, & Yutong Tan. (2023). Research on Optimal Design of Wireless Sensor Network with Particle Swarm Optimization and Improved Firefly Algorithm. 39. 1099–1104. 1 indexed citations
9.
Gong, Dong, Wei Yin, Hao Chen, et al.. (2023). Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes. 21539–21548. 16 indexed citations
10.
Sun, Wei, Dong Gong, Qinfeng Shi, Anton van den Hengel, & Yanning Zhang. (2022). Video super-resolution via mixed spatial-temporal convolution and selective fusion. Pattern Recognition. 126. 108577–108577. 8 indexed citations
11.
Lu, Wanxuan, Dong Gong, Kun Fu, et al.. (2021). Boundarymix: Generating pseudo-training images for improving segmentation with scribble annotations. Pattern Recognition. 117. 107924–107924. 7 indexed citations
12.
Yan, Qingsen, Bo Wang, Dong Gong, et al.. (2021). COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations. IEEE Transactions on Big Data. 7(1). 13–24. 79 indexed citations
13.
Sun, Wei, Dong Gong, Qinfeng Shi, Anton van den Hengel, & Yanning Zhang. (2021). Learning to Zoom-In via Learning to Zoom-Out: Real-World Super-Resolution by Generating and Adapting Degradation. IEEE Transactions on Image Processing. 30. 2947–2962. 21 indexed citations
14.
Zhang, Xinyu, Rufeng Zhang, Jiewei Cao, et al.. (2020). Part-Guided Attention Learning for Vehicle Instance Retrieval. IEEE Transactions on Intelligent Transportation Systems. 23(4). 3048–3060. 47 indexed citations
15.
Liu, Wenhe, Dong Gong, Mingkui Tan, et al.. (2019). Learning Distilled Graph for Large-Scale Social Network Data Clustering. IEEE Transactions on Knowledge and Data Engineering. 32(7). 1393–1404. 6 indexed citations
16.
Zhang, Xinyu, Rufeng Zhang, Jiewei Cao, et al.. (2019). Part-Guided Attention Learning for Vehicle Re-Identification.. arXiv (Cornell University). 21 indexed citations
17.
Gong, Dong, et al.. (2018). Blind image deblurring by promoting group sparsity. Neurocomputing. 310. 190–200. 9 indexed citations
18.
Sun, Jinqiu, et al.. (2018). ARSAC: Efficient model estimation via adaptively ranked sample consensus. Neurocomputing. 328. 88–96. 13 indexed citations
19.
Gong, Dong, Mingkui Tan, Yanning Zhang, Anton van den Hengel, & Qinfeng Shi. (2017). MPGL: An Efficient Matching Pursuit Method for Generalized LASSO. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 9 indexed citations
20.
Gong, Dong. (2001). Fast Motion Estimation Algorithm Using Horizontal and Multi-Grid Search Strategy. 362–365.

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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