Jinpeng Wang

1.6k citations
58 papers · 810 indexed · 1 hit paper · h-index 17
Topics
Multimodal Machine Learning Applications (15 papers)Domain Adaptation and Few-Shot Learning (14 papers)Topic Modeling (11 papers)

In The Last Decade

Jinpeng Wang

53 papers receiving 775 citations

Hit Papers

Evaluating Object Hallucination in Large Vision-Language ...202320262024202520234080120

Peers

Jinpeng Wang
Comparison fields: 5 of 89
  • Artificial Intelligence 502
  • Computer Vision and Pattern Recognition 457
  • Information Systems 65
  • Signal Processing 33
  • Biomedical Engineering 29
Replace Abhimanu Kumar with:
Abhimanu Kumar United States
Felix Wu United States
Yiheng Xu China
Zexuan Zhong United States
Elena Voita Netherlands
Yichun Yin China
Yonatan Bisk United States
Kehai Chen China
Da-Cheng Juan United States
Sainbayar Sukhbaatar United States
Jinpeng Wang relative to Abhimanu Kumar United States Abhimanu Kumar's profile →
Citations per field
00.5×10×20×25.2×
Abhimanu Kumar · 1×
Citations per year

Countries citing papers authored by Jinpeng Wang

Since Specialization
Citations

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

Fields of papers citing papers by Jinpeng Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jinpeng Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Jinpeng Wang. A scholar is included among the top collaborators of Jinpeng Wang 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 Jinpeng Wang. Jinpeng Wang 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
#WorkIndexed citations
1 0
2 0
3 1
4 5
5 1
6 6
7 1
8 1
9 11
10 2
11
Evaluating Object Hallucination in Large Vision-Language Modelsbreakdown →
128
12 1
13 79
14 49
15 55
16 7
17 16
18
A Statistical Framework for Product Description Generation
17
19
STUDY ON MULTI-HOP MIXED DIVERSITY ALGORITHM IN MULTI-HOP CELLULAR NETWORKS
0
20 5

About Jinpeng Wang

Jinpeng Wang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 58 papers that have together received 810 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (15 papers), Domain Adaptation and Few-Shot Learning (14 papers) and Topic Modeling (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (457 citations), Artificial Intelligence (502 citations) and Health Informatics (5 citations). Jinpeng Wang has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Chin-Yew Lin, Ji-Rong Wen, Yifan Li, Yifan Du, Rong Pan, J. Andy, Jin-Ge Yao, Mike Zheng Shou, Junyu Luo and Shuang Chen. Their work appears in journals such as Journal of Applied Physics, IEEE Transactions on Pattern Analysis and Machine Intelligence and Pattern Recognition.

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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