Haoye Dong

699 total citations
16 papers, 317 citations indexed

About

Haoye Dong is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Haoye Dong has authored 16 papers receiving a total of 317 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 6 papers in Computational Mechanics and 3 papers in Artificial Intelligence. Recurrent topics in Haoye Dong's work include Generative Adversarial Networks and Image Synthesis (10 papers), Advanced Vision and Imaging (8 papers) and 3D Shape Modeling and Analysis (6 papers). Haoye Dong is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (10 papers), Advanced Vision and Imaging (8 papers) and 3D Shape Modeling and Analysis (6 papers). Haoye Dong collaborates with scholars based in China, United States and Norway. Haoye Dong's co-authors include Xiaodan Liang, Jian Yin, Xiaohui Shen, Bowen Wu, Zhiting Hu, Jia Zhu, Hanjiang Lai, Bochao Wang, Xu‐Jie Zhang and Yixuan Zhang and has published in prestigious journals such as IEEE Transactions on Image Processing, arXiv (Cornell University) and Duo Research Archive (University of Oslo).

In The Last Decade

Haoye Dong

14 papers receiving 301 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Haoye Dong China 7 267 121 68 26 17 16 317
Shishir Subramanyam Netherlands 9 238 0.9× 92 0.8× 89 1.3× 7 0.3× 12 0.7× 20 325
Balázs Kovács United States 3 192 0.7× 53 0.4× 31 0.5× 33 1.3× 22 1.3× 3 233
Kenan E. Ak Singapore 9 255 1.0× 67 0.6× 16 0.2× 55 2.1× 35 2.1× 19 315
Yael Vinker Israel 8 276 1.0× 44 0.4× 97 1.4× 67 2.6× 14 0.8× 11 372
Subhadeep Koley United Kingdom 12 290 1.1× 29 0.2× 28 0.4× 64 2.5× 21 1.2× 25 356
Xianjing Han China 8 217 0.8× 69 0.6× 10 0.1× 77 3.0× 22 1.3× 10 278
Larry Gritz United States 9 203 0.8× 128 1.1× 214 3.1× 23 0.9× 22 1.3× 15 326
Yue Jiang Finland 7 162 0.6× 93 0.8× 84 1.2× 40 1.5× 11 0.6× 19 297
Cusuh Ham United States 4 349 1.3× 47 0.4× 51 0.8× 82 3.2× 19 1.1× 5 386
Alberto Baldrati Italy 7 231 0.9× 19 0.2× 17 0.3× 68 2.6× 8 0.5× 11 274

Countries citing papers authored by Haoye Dong

Since Specialization
Citations

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

Fields of papers citing papers by Haoye Dong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haoye Dong

This figure shows the co-authorship network connecting the top 25 collaborators of Haoye Dong. A scholar is included among the top collaborators of Haoye Dong 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 Haoye Dong. Haoye Dong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
2.
Torre, Fernando De la, et al.. (2024). Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba. 2127–2160.
3.
Dong, Haoye, et al.. (2024). Physical-space Multi-body Mesh Detection Achieved by Local Alignment and Global Dense Learning. 1256–1265. 2 indexed citations
4.
Dong, Haoye, et al.. (2024). DF-VTON: Dense Flow Guided Virtual Try-On Network. 3175–3179. 2 indexed citations
5.
Dong, Xinyong, et al.. (2023). GP-VTON: Towards General Purpose Virtual Try-On via Collaborative Local-Flow Global-Parsing Learning. 23550–23559. 34 indexed citations
6.
Li, Haoyuan, Haoye Dong, Dong Huang, et al.. (2023). Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos. Duo Research Archive (University of Oslo). 8710–8719. 3 indexed citations
7.
Wu, Bowen, et al.. (2021). Image Comes Dancing With Collaborative Parsing-Flow Video Synthesis. IEEE Transactions on Image Processing. 30. 9259–9269. 3 indexed citations
8.
Zhang, Xujie, et al.. (2021). WAS-VTON: Warping Architecture Search for Virtual Try-on Network. 3350–3359. 13 indexed citations
9.
Dong, Haoye, Xiaodan Liang, Yixuan Zhang, et al.. (2020). Fashion Editing With Adversarial Parsing Learning. 8117–8125. 56 indexed citations
10.
Dong, Haoye, Xiaodan Liang, Yixuan Zhang, et al.. (2019). Fashion Editing with Multi-scale Attention Normalization.. arXiv (Cornell University). 3 indexed citations
11.
Dong, Haoye, et al.. (2019). Part-Preserving Pose Manipulation for Person Image Synthesis. 1234–1239. 6 indexed citations
12.
Dong, Haoye, et al.. (2019). FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On. 1161–1170. 58 indexed citations
13.
Dong, Haoye, Xiaodan Liang, Xiaohui Shen, et al.. (2019). Towards Multi-Pose Guided Virtual Try-On Network. 9025–9034. 114 indexed citations
14.
Hu, Zhiting, Zichao Yang, Russ R. Salakhutdinov, et al.. (2018). Deep Generative Models with Learnable Knowledge Constraints. arXiv (Cornell University). 31. 10501–10512. 18 indexed citations
15.
Ding, Rui, et al.. (2016). A novel feature selection strategy for friends recommendation. 50. 123–128. 2 indexed citations
16.

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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