D-Index & Metrics Best Publications
Computer Science
South Korea
2022

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 64 Citations 19,425 450 World Ranking 1217 National Ranking 2

Research.com Recognitions

Awards & Achievements

2022 - Research.com Computer Science in South Korea Leader Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

In So Kweon mainly investigates Artificial intelligence, Computer vision, Convolutional neural network, Pattern recognition and Pixel. His works in Image, Feature extraction, RGB color model, Image segmentation and Object detection are all subjects of inquiry into Artificial intelligence. His research on Computer vision often connects related topics like Mobile robot.

His work carried out in the field of Convolutional neural network brings together such families of science as Artificial neural network, Inpainting, Feature learning and Code. His Pattern recognition research is multidisciplinary, incorporating elements of Histogram, Pascal, Feature and Canny edge detector. His Pixel research incorporates elements of High dynamic range, Specular highlight, Hue, Matrix completion and Specularity.

His most cited work include:

  • CBAM: Convolutional Block Attention Module (1526 citations)
  • Adaptive support-weight approach for correspondence search (1030 citations)
  • High quality depth map upsampling for 3D-TOF cameras (405 citations)

What are the main themes of his work throughout his whole career to date?

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Robustness and Image. His study in Feature extraction, Pixel, Cognitive neuroscience of visual object recognition, Convolutional neural network and Mobile robot is done as part of Artificial intelligence. Mobile robot navigation is the focus of his Mobile robot research.

His research on Computer vision frequently connects to adjacent areas such as Robot. As part of his studies on Pattern recognition, In So Kweon often connects relevant subjects like Feature.

He most often published in these fields:

  • Artificial intelligence (85.24%)
  • Computer vision (65.15%)
  • Pattern recognition (17.74%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (85.24%)
  • Computer vision (65.15%)
  • Deep learning (3.30%)

In recent papers he was focusing on the following fields of study:

In So Kweon mostly deals with Artificial intelligence, Computer vision, Deep learning, Robustness and Segmentation. His biological study spans a wide range of topics, including Machine learning and Pattern recognition. His study in Object, Depth map, Inpainting, Pixel and Motion estimation is carried out as part of his studies in Computer vision.

His studies in Deep learning integrate themes in fields like Artificial neural network, Information hiding, Feature and Network architecture. The study incorporates disciplines such as Normalization, Data mining, Feature and Feature extraction in addition to Robustness. In his study, which falls under the umbrella issue of Segmentation, Complement and Boundary representation is strongly linked to Basis.

Between 2018 and 2021, his most popular works were:

  • Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles (114 citations)
  • Learning Loss for Active Learning (97 citations)
  • DPSNet: End-to-end Deep Plane Sweep Stereo (68 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Statistics

In So Kweon mainly focuses on Artificial intelligence, Computer vision, Deep learning, Machine learning and Convolutional neural network. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Task and Pattern recognition. When carried out as part of a general Pattern recognition research project, his work on Pattern recognition is frequently linked to work in Compression artifact, therefore connecting diverse disciplines of study.

He combines topics linked to Unsupervised learning with his work on Computer vision. In So Kweon has researched Deep learning in several fields, including Network architecture, Sequence and Image, Inpainting. His work carried out in the field of Convolutional neural network brings together such families of science as Depth map, Iterative reconstruction and Sum of absolute differences.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

CBAM: Convolutional Block Attention Module

Sanghyun Woo;Jongchan Park;Joon-Young Lee;In So Kweon.
european conference on computer vision (2018)

1866 Citations

Adaptive support-weight approach for correspondence search

Kuk-Jin Yoon;In So Kweon.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

1569 Citations

High quality depth map upsampling for 3D-TOF cameras

Jaesik Park;Hyeongwoo Kim;Yu-Wing Tai;Michael S. Brown.
international conference on computer vision (2011)

531 Citations

A Tensor-Based Algorithm for High-Order Graph Matching

O. Duchenne;F. Bach;In-So Kweon;Jean Ponce.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2011)

486 Citations

Accurate depth map estimation from a lenslet light field camera

Hae-Gon Jeon;Jaesik Park;Gyeongmin Choe;Jinsun Park.
computer vision and pattern recognition (2015)

395 Citations

Multispectral pedestrian detection: Benchmark dataset and baseline

Soonmin Hwang;Jaesik Park;Namil Kim;Yukyung Choi.
computer vision and pattern recognition (2015)

394 Citations

Locally adaptive support-weight approach for visual correspondence search

Kuk-Jin Yoon;In-So Kweon.
computer vision and pattern recognition (2005)

332 Citations

High resolution terrain map from multiple sensor data

I.S. Kweon;T. Kanade.
intelligent robots and systems (1990)

243 Citations

Terrain mapping for a roving planetary explorer

M. Herbert;C. Caillas;E. Krotkov;I.S. Kweon.
international conference on robotics and automation (1989)

238 Citations

Extracting topographic terrain features from elevation maps

In So Kweon;Takeo Kanade.
Cvgip: Image Understanding (1994)

227 Citations

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