Hanlin Goh

773 total citations
20 papers, 256 citations indexed

About

Hanlin Goh is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, Hanlin Goh has authored 20 papers receiving a total of 256 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 6 papers in Aerospace Engineering and 5 papers in Artificial Intelligence. Recurrent topics in Hanlin Goh's work include Advanced Image and Video Retrieval Techniques (10 papers), Image Retrieval and Classification Techniques (6 papers) and Robotics and Sensor-Based Localization (6 papers). Hanlin Goh is often cited by papers focused on Advanced Image and Video Retrieval Techniques (10 papers), Image Retrieval and Classification Techniques (6 papers) and Robotics and Sensor-Based Localization (6 papers). Hanlin Goh collaborates with scholars based in Singapore, France and United States. Hanlin Goh's co-authors include Joo‐Hwee Lim, Nicolas Thome, Matthieu Cord, Vijay Chandrasekhar, Antoine Veillard, Jie Lin, Vassilios G. Agelidis, Nitish Srivastava, Tat-Jun Chin and Ruslan Salakhutdinov and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Signal Processing and International Journal of Systems Science.

In The Last Decade

Hanlin Goh

20 papers receiving 249 citations

Peers

Hanlin Goh
Comparison fields: 5 of 60
  • Computer Vision and Pattern Recognition 163
  • Artificial Intelligence 74
  • Media Technology 46
  • Electrical and Electronic Engineering 25
  • Aerospace Engineering 18
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Chen Guo China
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Zhangzhang Si United States
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Citations per field, relative to Hanlin Goh
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Citations per year, relative to Hanlin Goh
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Countries citing papers authored by Hanlin Goh

Since Specialization
Citations

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

Fields of papers citing papers by Hanlin Goh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hanlin Goh

This figure shows the co-authorship network connecting the top 25 collaborators of Hanlin Goh. A scholar is included among the top collaborators of Hanlin Goh 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 Hanlin Goh. Hanlin Goh 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
# Work Indexed citations
1 2
2
Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
21
3 8
4 16
5 3
6 55
7 1
8 8
9 9
10 78
11 10
12
I2R ImageCLEF Photo Annotation 2009 Working Notes
3
13 7
14 2
15 6
16 6
17 6
18 1
19 10
20 4

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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