Daniel Kang

1.9k citations
34 papers · 666 indexed · h-index 13
Topics
Advanced Image and Video Retrieval Techniques (8 papers)Human Pose and Action Recognition (5 papers)Machine Learning and Algorithms (3 papers)

In The Last Decade

Daniel Kang

30 papers receiving 637 citations

Peers

Daniel Kang
Comparison fields: 5 of 77
  • Computer Vision and Pattern Recognition 347
  • Artificial Intelligence 211
  • Computer Networks and Communications 91
  • Information Systems 56
  • Signal Processing 46
Replace Nicky Kern with:
Nicky Kern Switzerland
Alexandros André Chaaraoui Spain
Rizwan Ahmed Khan Pakistan
Kristina Yordanova Germany
Mei Kuan Lim Malaysia
Haibo Tian China
Richard Guest United Kingdom
Dohyung Kim South Korea
Neelu Jyothi Ahuja India
Joseph Alexander Brown Canada
Daniel Kang relative to Nicky Kern Switzerland Nicky Kern's profile →
Citations per field
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Nicky Kern · 1×
Citations per year

Countries citing papers authored by Daniel Kang

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Kang. A scholar is included among the top collaborators of Daniel Kang 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 Daniel Kang. Daniel Kang 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
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5 10
6 8
7 13
8 1
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10 11
11 12
12
Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine.
11
13
LIT: Learned Intermediate Representation Training for Model Compression
19
14
BlazeIt: Fast Exploratory Video Queries using Neural Networks.
13
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NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale.
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Optimizing Deep CNN-Based Queries over Video Streams at Scale
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17 1
18 12
19 30
20 87

About Daniel Kang

Daniel Kang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and General Social Sciences, having authored 34 papers that have together received 666 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (8 papers), Human Pose and Action Recognition (5 papers) and Machine Learning and Algorithms (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (347 citations), Artificial Intelligence (211 citations) and Physical Therapy, Sports Therapy and Rehabilitation (19 citations). Daniel Kang has collaborated with scholars based in United States, Jamaica and Germany. Frequent co-authors include Matei Zaharia, Peter Bailis, John Emmons, Firas Abuzaid, Tatsunori Hashimoto, David K. Gifford, Haoyang Zeng, Natasha A. Emmerson, Kimberley D. Lakes and Ion Stoica. Their work appears in journals such as Bioinformatics, PLoS ONE and Genome Research.

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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