H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 144 Citations 167,950 487 World Ranking 16 National Ranking 10

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary scientific interests are in Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Segmentation. His Artificial intelligence study frequently links to adjacent areas such as Natural language processing. His Pattern recognition study combines topics from a wide range of disciplines, such as Object detection and Feature.

The Machine learning study combines topics in areas such as Adversarial system, Training set and Domain. His biological study spans a wide range of topics, including Pixel, Image, Pascal and Feature learning. His Pascal research incorporates themes from Layer and Inference.

His most cited work include:

  • Fully convolutional networks for semantic segmentation (15497 citations)
  • Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (12438 citations)
  • Caffe: Convolutional Architecture for Fast Feature Embedding (8817 citations)

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

His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Object. As a part of the same scientific study, he usually deals with the Artificial intelligence, concentrating on Natural language processing and frequently concerns with Closed captioning. His Pattern recognition research incorporates elements of Contextual image classification and Feature.

He has researched Computer vision in several fields, including Robot and Representation. The various areas that he examines in his Machine learning study include Training set and Inference. His Object detection study integrates concerns from other disciplines, such as Pascal and Minimum bounding box.

He most often published in these fields:

  • Artificial intelligence (78.29%)
  • Pattern recognition (27.98%)
  • Computer vision (24.31%)

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

  • Artificial intelligence (78.29%)
  • Machine learning (23.70%)
  • Pattern recognition (27.98%)

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

His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Sequence and Benchmark. His Artificial intelligence study combines topics in areas such as Computer vision and Natural language processing. His Machine learning research is multidisciplinary, relying on both Generalization and Baseline.

Trevor Darrell interconnects Domain adaptation, Entropy and Inference in the investigation of issues within Pattern recognition. His Benchmark study incorporates themes from Natural language and Set. His Segmentation research focuses on subjects like Structure, which are linked to Representation.

Between 2018 and 2021, his most popular works were:

  • Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders (184 citations)
  • Few-Shot Object Detection via Feature Reweighting (116 citations)
  • Hierarchical Discrete Distribution Decomposition for Match Density Estimation (107 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

Trevor Darrell mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Classifier and Visualization. Artificial intelligence and Computer vision are frequently intertwined in his study. As part of one scientific family, Trevor Darrell deals mainly with the area of Machine learning, narrowing it down to issues related to the Generalization, and often Robotic arm, Link, Modularity, Baseline and Classifier.

The study incorporates disciplines such as Cognitive neuroscience of visual object recognition, Receptive field and Closed captioning in addition to Pattern recognition. Trevor Darrell focuses mostly in the field of Classifier, narrowing it down to matters related to Relational reasoning and, in some cases, Knowledge extraction and Theoretical computer science. His Visualization research integrates issues from Vocabulary, Pooling and Transformer.

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

Fully convolutional networks for semantic segmentation

Jonathan Long;Evan Shelhamer;Trevor Darrell.
computer vision and pattern recognition (2015)

17007 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

Caffe: Convolutional Architecture for Fast Feature Embedding

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

10029 Citations

Pfinder: real-time tracking of the human body

C.R. Wren;A. Azarbayejani;T. Darrell;A.P. Pentland.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)

6663 Citations

Long-term recurrent convolutional networks for visual recognition and description

Jeff Donahue;Lisa Anne Hendricks;Sergio Guadarrama;Marcus Rohrbach.
computer vision and pattern recognition (2015)

4201 Citations

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Jeff Donahue;Yangqing Jia;Oriol Vinyals;Judy Hoffman.
international conference on machine learning (2014)

3416 Citations

Fully Convolutional Networks for Semantic Segmentation

Evan Shelhamer;Jonathan Long;Trevor Darrell.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)

1942 Citations

The pyramid match kernel: discriminative classification with sets of image features

K. Grauman;T. Darrell.
international conference on computer vision (2005)

1884 Citations

End-to-end training of deep visuomotor policies

Sergey Levine;Chelsea Finn;Trevor Darrell;Pieter Abbeel.
Journal of Machine Learning Research (2016)

1742 Citations

Adapting visual category models to new domains

Kate Saenko;Brian Kulis;Mario Fritz;Trevor Darrell.
european conference on computer vision (2010)

1506 Citations

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Best Scientists Citing Trevor Darrell

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Sun Yat-sen University

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Chinese University of Hong Kong

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