Humphrey Shi

12.7k citations
93 papers · 4.2k indexed · 9 hit papers · h-index 30
Topics
Advanced Neural Network Applications (29 papers)Domain Adaptation and Few-Shot Learning (19 papers)Generative Adversarial Networks and Image Synthesis (18 papers)

In The Last Decade

Humphrey Shi

87 papers receiving 4.1k citations

Hit Papers

Self-Similarity Grouping: A Simple Unsupervised Cross Dom...201920262021202320192020201920192020100200300

Peers

Humphrey Shi
Comparison fields: 5 of 152
  • Computer Vision and Pattern Recognition 3.2k
  • Artificial Intelligence 1.0k
  • Media Technology 718
  • Biomedical Engineering 500
  • Radiology, Nuclear Medicine and Imaging 267
Replace Mingming Gong with:
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Citations per field
00.5×3.4×
Mingming Gong · 1×
Citations per year

Countries citing papers authored by Humphrey Shi

Since Specialization
Citations

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

Fields of papers citing papers by Humphrey Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Humphrey Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Humphrey Shi. A scholar is included among the top collaborators of Humphrey Shi 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 Humphrey Shi. Humphrey Shi 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 1
3 7
4 22
5 0
6 1
7 44
8 3
9 5
10 5
11 2
12 58
13 5
14 10
15 23
16
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
11
17
Bottom-up Higher-Resolution Networks for Multi-Person Pose Estimation
22
18
Revisiting Pre-training: An Efficient Training Method for Image Classification
2
19
One Shot Domain Adaptation for Person Re-Identification
1
20
Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids.
20

About Humphrey Shi

Humphrey Shi is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Media Technology, having authored 93 papers that have together received 4.2k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (29 papers), Domain Adaptation and Few-Shot Learning (19 papers) and Generative Adversarial Networks and Image Synthesis (18 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (3.2k citations), Media Technology (718 citations) and Artificial Intelligence (1.0k citations). Humphrey Shi has collaborated with scholars based in United States, China and Jamaica. Frequent co-authors include Thomas S. Huang, Yunchao Wei, Yuqian Zhou, Zilong Huang, Jiachen Li, Yang Fu, Lichao Huang, Rogério Feris, Ali Hassani and Xinggang Wang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Cement and Concrete Research and IEEE Transactions on Image Processing.

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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