D-Index & Metrics Best Publications

D-Index & Metrics 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.

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 54 Citations 11,885 244 World Ranking 3008 National Ranking 298

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Feature extraction are his primary areas of study. All of his Artificial intelligence and Visualization, Image processing, Discriminative model, Deep learning and Object detection investigations are sub-components of the entire Artificial intelligence study. His work in the fields of Computer vision, such as Segmentation, Image segmentation and Cognitive neuroscience of visual object recognition, intersects with other areas such as Gait analysis.

He has included themes like Embedding, Pascal and Robustness in his Pattern recognition study. His study on Artificial neural network and Re identification is often connected to Term and Benchmark as part of broader study in Machine learning. His Feature extraction study integrates concerns from other disciplines, such as Variation, Hough transform, Radon transform and Feature.

His most cited work include:

  • Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification (595 citations)
  • Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification (398 citations)
  • Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection (292 citations)

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

Kaiqi Huang spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Machine learning. His Artificial intelligence research focuses on Object detection, Robustness, Contextual image classification, Cognitive neuroscience of visual object recognition and Visualization. His work carried out in the field of Robustness brings together such families of science as Particle filter and Pose.

In his study, which falls under the umbrella issue of Pattern recognition, Data mining is strongly linked to Pascal. His Feature extraction research is multidisciplinary, incorporating elements of Feature learning, Task analysis and Hidden Markov model. His Machine learning study incorporates themes from Representation, Task and Set.

He most often published in these fields:

  • Artificial intelligence (85.47%)
  • Computer vision (47.01%)
  • Pattern recognition (39.32%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (85.47%)
  • Computer vision (47.01%)
  • Pattern recognition (39.32%)

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

His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Machine learning. His research on Artificial intelligence often connects related areas such as Task analysis. His research in Computer vision tackles topics such as Window which are related to areas like Aggregate and Task.

Kaiqi Huang works mostly in the field of Pattern recognition, limiting it down to topics relating to Pascal and, in certain cases, Robustness and Convolution, as a part of the same area of interest. His Feature extraction study combines topics from a wide range of disciplines, such as Margin and Minimum bounding box. His biological study spans a wide range of topics, including Adversarial system, Artificial neural network and Theoretical computer science.

Between 2017 and 2021, his most popular works were:

  • GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild (200 citations)
  • Adversarially Occluded Samples for Person Re-identification (104 citations)
  • Fast End-to-End Trainable Guided Filter (94 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

The scientist’s investigation covers issues in Artificial intelligence, Feature extraction, Computer vision, Pattern recognition and Image. His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning. His study in Machine learning is interdisciplinary in nature, drawing from both Image quality, Cognitive neuroscience of visual object recognition and Spatial relation.

His Feature extraction research is multidisciplinary, incorporating perspectives in Visualization and Minimum bounding box. Kaiqi Huang interconnects Deep learning and Code in the investigation of issues within Computer vision. His Pattern recognition research focuses on Task and how it relates to Structure, Pose and Shot.

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

The Visual Object Tracking VOT2013 Challenge Results

Matej Kristan;Roman Pflugfelder;Ale Leonardis;Jiri Matas.
international conference on computer vision (2013)

1356 Citations

Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification

Weihua Chen;Xiaotang Chen;Jianguo Zhang;Kaiqi Huang.
computer vision and pattern recognition (2017)

788 Citations

Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification

Dangwei Li;Xiaotang Chen;Zhang Zhang;Kaiqi Huang.
computer vision and pattern recognition (2017)

543 Citations

GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

Lianghua Huang;Xin Zhao;Kaiqi Huang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)

461 Citations

Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection

Min Li;Zhaoxiang Zhang;Kaiqi Huang;Tieniu Tan.
international conference on pattern recognition (2008)

442 Citations

A Study on Gait-Based Gender Classification

Shiqi Yu;Tieniu Tan;Kaiqi Huang;Kui Jia.
IEEE Transactions on Image Processing (2009)

351 Citations

Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes

Zhang Zhang;Kaiqi Huang;Tieniu Tan.
international conference on pattern recognition (2006)

330 Citations

Robust view transformation model for gait recognition

Shuai Zheng;Junge Zhang;Kaiqi Huang;Ran He.
international conference on image processing (2011)

264 Citations

Weakly Supervised Object Localization with Latent Category Learning

Chong Wang;Weiqiang Ren;Kaiqi Huang;Tieniu Tan.
european conference on computer vision (2014)

215 Citations

Human Activity Recognition Based on R Transform

Ying Wang;Kaiqi Huang;Tieniu Tan.
computer vision and pattern recognition (2007)

214 Citations

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Best Scientists Citing Kaiqi Huang

Dacheng Tao

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Chinese Academy of Sciences

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Chinese Academy of Sciences

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