H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 38 Citations 5,874 133 World Ranking 5028 National Ranking 2475

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Yan Yan spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Multi-task learning. His study in Feature, Convolutional neural network, Training set, Support vector machine and Face is done as part of Artificial intelligence. In his study, which falls under the umbrella issue of Feature, Noise reduction, Motion, Contrast and Matching is strongly linked to Representation.

His study looks at the relationship between Pattern recognition and topics such as Deep learning, which overlap with Anomaly detection. His research investigates the link between Machine learning and topics such as Visualization that cross with problems in Semi-supervised learning. His study looks at the intersection of Discriminative model and topics like Feature extraction with Landmark.

His most cited work include:

  • Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (245 citations)
  • Fortune teller: predicting your career path (200 citations)
  • Detecting anomalous events in videos by learning deep representations of appearance and motion (176 citations)

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

Yan Yan mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Discriminative model. Artificial intelligence is represented through his Feature, Deep learning, Image, Pose and Face research. His Feature selection and Classifier study in the realm of Pattern recognition connects with subjects such as Invariant and Constraint.

Yan Yan regularly ties together related areas like Contextual image classification in his Machine learning studies. When carried out as part of a general Computer vision research project, his work on Optical flow and Gaze is frequently linked to work in GPS signals, therefore connecting diverse disciplines of study. His studies deal with areas such as Object and Feature extraction as well as Discriminative model.

He most often published in these fields:

  • Artificial intelligence (72.58%)
  • Pattern recognition (27.96%)
  • Machine learning (24.19%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (72.58%)
  • Modal (4.30%)
  • Applied mathematics (9.14%)

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

His primary scientific interests are in Artificial intelligence, Modal, Applied mathematics, Pattern recognition and Computer vision. His Artificial intelligence study frequently links to related topics such as Machine learning. His research integrates issues of Object and Conditional random field in his study of Machine learning.

His biological study deals with issues like Smooth pursuit, which deal with fields such as Statistical algorithm and Robustness. His work in the fields of Computer vision, such as Enhanced Data Rates for GSM Evolution, Shadow and Image, overlaps with other areas such as Distortion and Market segmentation. Task and Semantics is closely connected to Discriminative model in his research, which is encompassed under the umbrella topic of Feature.

Between 2019 and 2021, his most popular works were:

  • Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation (28 citations)
  • Segmenting Objects in Day and Night: Edge-Conditioned CNN for Thermal Image Semantic Segmentation. (11 citations)
  • Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation (9 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

Yan Yan mostly deals with Artificial intelligence, Applied mathematics, Duality gap, Convexity and Feature. His Artificial intelligence research integrates issues from Machine learning and Computer vision. His work on Margin as part of general Machine learning research is frequently linked to Rank, bridging the gap between disciplines.

Yan Yan combines subjects such as Gradient descent and Stochastic gradient descent with his study of Applied mathematics. Yan Yan has included themes like Image generation and Generative adversarial network in his Feature study. The various areas that Yan Yan examines in his Artificial neural network study include Contrast, Computation and Canonical correlation.

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.

Top Publications

Fortune teller: predicting your career path

Ye Liu;Luming Zhang;Liqiang Nie;Yan Yan.
national conference on artificial intelligence (2016)

243 Citations

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

Dan Xu;Elisa Ricci;Yan Yan;Jingkuan Song.
british machine vision conference (2015)

217 Citations

Detecting anomalous events in videos by learning deep representations of appearance and motion

Dan Xu;Yan Yan;Elisa Ricci;Elisa Ricci;Nicu Sebe.
Computer Vision and Image Understanding (2017)

216 Citations

Person Re-identification via Recurrent Feature Aggregation

Yichao Yan;Bingbing Ni;Zhichao Song;Chao Ma.
european conference on computer vision (2016)

207 Citations

Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning

Yu Wu;Yutian Lin;Xuanyi Dong;Yan Yan.
computer vision and pattern recognition (2018)

172 Citations

Multitask linear discriminant analysis for view invariant action recognition.

Yan Yan;Elisa Ricci;Ramanathan Subramanian;Gaowen Liu.
IEEE Transactions on Image Processing (2014)

167 Citations

Semisupervised Feature Selection via Spline Regression for Video Semantic Recognition

Yahong Han;Yi Yang;Yan Yan;Zhigang Ma.
IEEE Transactions on Neural Networks (2015)

161 Citations

A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification

Yutian Lin;Xuanyi Dong;Liang Zheng;Yan Yan.
national conference on artificial intelligence (2019)

151 Citations

Recurrent Face Aging

Wei Wang;Zhen Cui;Yan Yan;Jiashi Feng.
computer vision and pattern recognition (2016)

148 Citations

Egocentric Daily Activity Recognition via Multitask Clustering

Yan Yan;Elisa Ricci;Gaowen Liu;Nicu Sebe.
IEEE Transactions on Image Processing (2015)

142 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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Top Scientists Citing Yan Yan

Yi Yang

Yi Yang

Zhejiang University

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Inception Institute of Artificial Intelligence

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Liang Zheng

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Australian National University

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Feiping Nie

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Northwestern Polytechnical University

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Xuelong Li

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Northwestern Polytechnical University

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Heng Tao Shen

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Alexander G. Hauptmann

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Carnegie Mellon University

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Carnegie Mellon University

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