H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 66 Citations 203,060 103 World Ranking 1098 National Ranking 652

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Ross Girshick mainly investigates Artificial intelligence, Object detection, Artificial neural network, Computer vision and Feature extraction. His Artificial intelligence research includes themes of Machine learning and Pattern recognition. His Pattern recognition study integrates concerns from other disciplines, such as Cognitive neuroscience of visual object recognition and Feature.

Ross Girshick interconnects Frame rate, Pascal and Support vector machine in the investigation of issues within Object detection. Ross Girshick has included themes like Contextual image classification and Cardinality, Set in his Artificial neural network study. Ross Girshick combines subjects such as Image segmentation and Robustness with his study of Feature extraction.

His most cited work include:

  • Faster R-CNN: towards real-time object detection with region proposal networks (12666 citations)
  • Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (12438 citations)
  • You Only Look Once: Unified, Real-Time Object Detection (8892 citations)

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

Ross Girshick mainly focuses on Artificial intelligence, Object detection, Machine learning, Pattern recognition and Computer vision. His Artificial intelligence study focuses mostly on Segmentation, Object, Convolutional neural network, Artificial neural network and Feature extraction. The Feature extraction study combines topics in areas such as Visualization and Pose.

His Object detection research is multidisciplinary, incorporating perspectives in Contextual image classification, Pascal and Minimum bounding box. His Machine learning research is multidisciplinary, incorporating elements of Training set and Code. His study explores the link between Pattern recognition and topics such as Feature that cross with problems in Embedding.

He most often published in these fields:

  • Artificial intelligence (91.03%)
  • Object detection (41.67%)
  • Machine learning (33.33%)

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

  • Artificial intelligence (91.03%)
  • Segmentation (24.36%)
  • Machine learning (33.33%)

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

Ross Girshick mostly deals with Artificial intelligence, Segmentation, Machine learning, Artificial neural network and Code. His Artificial intelligence study incorporates themes from Computer vision and Pattern recognition. His Segmentation research incorporates elements of Object, Unsupervised learning, Pascal, Feature learning and Task.

His Machine learning research incorporates themes from Training set and Benchmark. His Artificial neural network research integrates issues from Region of interest, Sampling and Image. His research in Object detection intersects with topics in Property, Initialization, Key and Regularization.

Between 2018 and 2021, his most popular works were:

  • Focal Loss for Dense Object Detection (957 citations)
  • Momentum Contrast for Unsupervised Visual Representation Learning (939 citations)
  • Mask R-CNN (457 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of investigation include Artificial intelligence, Segmentation, Computer vision, Object detection and Machine learning. His Artificial intelligence study frequently draws parallels with other fields, such as Code. In Segmentation, Ross Girshick works on issues like Task, which are connected to Pixel, Class and Pyramid.

His Computer vision study frequently links to adjacent areas such as Artificial neural network. His work deals with themes such as Initialization and Training set, which intersect with Machine learning. His Image segmentation course of study focuses on Feature extraction and Contextual image classification, Entropy, Convolutional neural network and Minimum bounding box.

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

You Only Look Once: Unified, Real-Time Object Detection

Joseph Redmon;Santosh Divvala;Ross Girshick;Ali Farhadi.
computer vision and pattern recognition (2016)

15365 Citations

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Ross Girshick;Jeff Donahue;Trevor Darrell;Jitendra Malik.
computer vision and pattern recognition (2014)

14643 Citations

Mask R-CNN

Kaiming He;Georgia Gkioxari;Piotr Dollar;Ross Girshick.
international conference on computer vision (2017)

12009 Citations

Fast R-CNN

Ross Girshick.
international conference on computer vision (2015)

11886 Citations

Faster R-CNN: towards real-time object detection with region proposal networks

Shaoqing Ren;Kaiming He;Ross Girshick;Jian Sun.
neural information processing systems (2015)

11334 Citations

Object Detection with Discriminatively Trained Part-Based Models

P F Felzenszwalb;R B Girshick;D McAllester;D Ramanan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)

10895 Citations

Caffe: Convolutional Architecture for Fast Feature Embedding

Yangqing Jia;Evan Shelhamer;Jeff Donahue;Sergey Karayev.
acm multimedia (2014)

10029 Citations

Microsoft COCO: Common Objects in Context

Tsung-Yi Lin;Michael Maire;Serge Belongie;Lubomir Bourdev.
arXiv: Computer Vision and Pattern Recognition (2014)

6948 Citations

Focal Loss for Dense Object Detection

Tsung-Yi Lin;Priya Goyal;Ross Girshick;Kaiming He.
international conference on computer vision (2017)

4840 Citations

Feature Pyramid Networks for Object Detection

Tsung-Yi Lin;Piotr Dollar;Ross Girshick;Kaiming He.
computer vision and pattern recognition (2017)

4162 Citations

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Best Scientists Citing Ross Girshick

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Luc Van Gool

ETH Zurich

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Shuicheng Yan

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Qi Tian

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Huawei Technologies (China)

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Xiaogang Wang

Xiaogang Wang

Chinese University of Hong Kong

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Liang Lin

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Sun Yat-sen University

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Trevor Darrell

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University of California, Berkeley

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Dacheng Tao

Dacheng Tao

University of Sydney

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Jiashi Feng

Jiashi Feng

National University of Singapore

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Yi Yang

Yi Yang

Zhejiang University

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Larry S. Davis

Larry S. Davis

University of Maryland, College Park

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Wanli Ouyang

University of Sydney

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Ming-Hsuan Yang

Ming-Hsuan Yang

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Bernt Schiele

Bernt Schiele

Max Planck Institute for Informatics

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Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

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