H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 132 Citations 125,734 335 World Ranking 28 National Ranking 1


What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Her scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Contextual image classification and Machine learning. Optical flow, Feature extraction, Histogram, Image processing and Image retrieval are among the areas of Artificial intelligence where the researcher is concentrating her efforts. Cordelia Schmid interconnects Kernel and Visual Word in the investigation of issues within Pattern recognition.

Her Computer vision research focuses on subjects like Pattern recognition, which are linked to Point, Representation, Object-class detection, Object and Active shape model. Her study on Contextual image classification also encompasses disciplines like

  • Support vector machine that intertwine with fields like Classifier,
  • Embedding and related Text mining and Linear programming. Her work carried out in the field of Machine learning brings together such families of science as Data set and Metric.

Her most cited work include:

  • Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories (7096 citations)
  • A performance evaluation of local descriptors (5974 citations)
  • Learning realistic human actions from movies (3082 citations)

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

Cordelia Schmid focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Image. Her study in Object detection, Contextual image classification, Object, Segmentation and Convolutional neural network is done as part of Artificial intelligence. While the research belongs to areas of Pattern recognition, Cordelia Schmid spends her time largely on the problem of Image retrieval, intersecting her research to questions surrounding Search engine indexing.

Her Affine transformation research extends to Computer vision, which is thematically connected. The concepts of her Image study are interwoven with issues in Matching, State and Data mining. Her Cognitive neuroscience of visual object recognition study combines topics from a wide range of disciplines, such as Image processing and Invariant.

She most often published in these fields:

  • Artificial intelligence (85.89%)
  • Pattern recognition (38.79%)
  • Computer vision (37.78%)

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

  • Artificial intelligence (85.89%)
  • Machine learning (20.91%)
  • Pattern recognition (38.79%)

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

Her primary areas of study are Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Image. Object, Classifier, Leverage, Motion and Pose are the subjects of her Artificial intelligence studies. Her Machine learning research includes themes of Object detection, Robot and Benchmark.

Cordelia Schmid studies Feature vector, a branch of Pattern recognition. Her work on Optical flow and RGB color model as part of general Computer vision study is frequently connected to Generalization, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. Her Image research is multidisciplinary, incorporating perspectives in Probabilistic logic, Generative grammar and Convolution.

Between 2018 and 2021, her most popular works were:

  • VideoBERT: A Joint Model for Video and Language Representation Learning (244 citations)
  • LCR-Net++: Multi-Person 2D and 3D Pose Detection in Natural Images (146 citations)
  • Learning Joint Reconstruction of Hands and Manipulated Objects (101 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

Artificial intelligence, Machine learning, Computer vision, Leverage and Training set are her primary areas of study. Her Artificial intelligence research incorporates elements of Natural language processing and Pattern recognition. In the field of Pattern recognition, her study on Classifier overlaps with subjects such as Locality.

Her Machine learning research also works with subjects such as

  • Object detection which connect with Feature learning, Mutual information, Segmentation and Overfitting,
  • Pascal and related Image segmentation. Her Training set study incorporates themes from Black box, Logistic regression, Inference and White box. Her biological study spans a wide range of topics, including Artificial neural network, Motion, Cognitive neuroscience of visual object recognition and Motion estimation.

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

A performance evaluation of local descriptors

K. Mikolajczyk;C. Schmid.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)

9714 Citations

Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

S. Lazebnik;C. Schmid;J. Ponce.
computer vision and pattern recognition (2006)

9333 Citations

Scale & Affine Invariant Interest Point Detectors

Krystian Mikolajczyk;Cordelia Schmid.
International Journal of Computer Vision (2004)

5179 Citations

A Comparison of Affine Region Detectors

K. Mikolajczyk;T. Tuytelaars;C. Schmid;A. Zisserman.
International Journal of Computer Vision (2005)

3982 Citations

Learning realistic human actions from movies

I. Laptev;M. Marszalek;C. Schmid;B. Rozenfeld.
computer vision and pattern recognition (2008)

3828 Citations

Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

Jianguo Zhang;M. Marszalek;S. Lazebnik;C. Schmid.
computer vision and pattern recognition (2006)

2825 Citations

Action Recognition with Improved Trajectories

Heng Wang;Cordelia Schmid.
international conference on computer vision (2013)

2616 Citations

Action recognition by dense trajectories

Heng Wang;Alexander Klaser;Cordelia Schmid;Cheng-Lin Liu.
computer vision and pattern recognition (2011)

2309 Citations

Evaluation of Interest Point Detectors

Cordelia Schmid;Roger Mohr;Christian Bauckhage.
International Journal of Computer Vision (2000)

2290 Citations

Local grayvalue invariants for image retrieval

C. Schmid;R. Mohr.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)

2239 Citations

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Best Scientists Citing Cordelia Schmid

Qi Tian

Qi Tian

Huawei Technologies (China)

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Andrew Zisserman

Andrew Zisserman

University of Oxford

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Luc Van Gool

ETH Zurich

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Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

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Dacheng Tao

Dacheng Tao

University of Sydney

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Bernt Schiele

Bernt Schiele

Max Planck Institute for Informatics

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Shuicheng Yan

Shuicheng Yan

National University of Singapore

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Larry S. Davis

Larry S. Davis

University of Maryland, College Park

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Alexander G. Hauptmann

Alexander G. Hauptmann

Carnegie Mellon University

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Shih-Fu Chang

Shih-Fu Chang

Columbia University

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Yi Yang

Yi Yang

Zhejiang University

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Junsong Yuan

Junsong Yuan

University at Buffalo, State University of New York

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Tinne Tuytelaars

Tinne Tuytelaars

KU Leuven

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Xuelong Li

Xuelong Li

Northwestern Polytechnical University

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Li Fei-Fei

Li Fei-Fei

Stanford University

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Pascal Fua

Pascal Fua

École Polytechnique Fédérale de Lausanne

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