Haochen Wang

948 citations
46 papers · 573 · 1 hit paper · h-index 10

Impact in

Papers in

Haochen Wang

38 papers receiving 564 citations

Haochen Wang's Hit Papers

Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels 2022 · 278 citations
2780+1+2Years since publication50100150200250

Peers

Haochen Wang
Comparison fields: 5 of 92
  • Computer Vision and Pattern Recognition 247
  • Media Technology 57
  • Ocean Engineering 95
  • Artificial Intelligence 189
  • Software 20
Replace Jakob Gawlikowski with:
Jakob Gawlikowski Germany
Xiaoya Li China
Jianxiang Feng China
Matthias Humt Germany
Laurent Valentin Jospin Switzerland
Yuxian Meng China
Jianbo Liu China
Haochen Wang relative to Jakob Gawlikowski Germany Jakob Gawlikowski's profile →
Citations per field
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Jakob Gawlikowski · 1×
Citations per year

Countries citing papers authored by Haochen Wang

Since Specialization
Citations

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

Fields of papers citing papers by Haochen Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Haochen Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Haochen Wang Line = papers co-authored together Haochen Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Hit paper breakdown →
2022278
2 202186
3 201832
4 202316
5 202115
6 202113
7 202113
8 202210
9 202110
10 202210
11 20239
12 20128
13 20237
14 20236
15 20156
16 20235
17 20225
18 20205
19 20214
20 20244

About Haochen Wang

Haochen Wang is a scholar working on Ocean Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Mechanical Engineering and Control and Systems Engineering, having authored 46 papers that have together received 573 indexed citations. Recurring topics across this work include Reservoir Engineering and Simulation Methods (13 papers), Hydraulic Fracturing and Reservoir Analysis (9 papers), Advanced Neural Network Applications (8 papers), Domain Adaptation and Few-Shot Learning (7 papers), Multimodal Machine Learning Applications (6 papers), Oil and Gas Production Techniques (4 papers), Drilling and Well Engineering (4 papers) and Hydrocarbon exploration and reservoir analysis (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (247 citations), Media Technology (57 citations), Ocean Engineering (95 citations), Artificial Intelligence (189 citations) and Software (20 citations). Haochen Wang has collaborated with scholars based in China, United States and Netherlands. Frequent co-authors include Xinyi Le, Yujun Shen, Liwei Wu, Guoqiang Jin, Rui Zhao, Wei Li, Kai Zhang, Xiaopeng Ma, Jun Yao and Jian Wang. Their work appears in journals such as SPE Journal, Applied Sciences, Journal of Intelligent & Fuzzy Systems, Energies and Physical Review Applied.

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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