H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 70 Citations 58,814 156 World Ranking 853 National Ranking 510

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Artificial intelligence, Computer vision, Object detection, Pattern recognition and Machine learning are her primary areas of study. Her work on Discriminative model, Pascal, Pose and Cognitive neuroscience of visual object recognition as part of her general Artificial intelligence study is frequently connected to User interface, thereby bridging the divide between different branches of science. Her research integrates issues of Visualization and Benchmark in her study of Computer vision.

Deva Ramanan has included themes like Cluster analysis, Support vector machine and Active appearance model in her Object detection study. Her Pattern recognition study incorporates themes from Representation and Face. Her biological study spans a wide range of topics, including Contextual image classification, Object, Training set and Viola–Jones object detection framework.

Her most cited work include:

  • Microsoft COCO: Common Objects in Context (10541 citations)
  • Object Detection with Discriminatively Trained Part-Based Models (7772 citations)
  • Microsoft COCO: Common Objects in Context (3312 citations)

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

Her scientific interests lie mostly in Artificial intelligence, Computer vision, Machine learning, Pattern recognition and Object detection. Her Object, Segmentation, Pose, Discriminative model and Benchmark investigations are all subjects of Artificial intelligence research. Deva Ramanan interconnects Probabilistic latent semantic analysis and Latent variable in the investigation of issues within Discriminative model.

The various areas that Deva Ramanan examines in her Machine learning study include Contextual image classification and Training set. Her research integrates issues of Representation, Face and Feature in her study of Pattern recognition. The Object detection study combines topics in areas such as Image segmentation and Pascal.

She most often published in these fields:

  • Artificial intelligence (89.71%)
  • Computer vision (36.76%)
  • Machine learning (31.37%)

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

  • Artificial intelligence (89.71%)
  • Computer vision (36.76%)
  • Object (15.20%)

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

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Object, Object detection and Segmentation. Her Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Pattern recognition. Her Optical flow study in the realm of Computer vision interacts with subjects such as Scale, Lidar and Flow.

Her Object research also works with subjects such as

  • Intelligent agent and F1 score most often made with reference to Tracking,
  • RGB color model together with Conditional independence and Minimum bounding box. Her study focuses on the intersection of Object detection and fields such as Benchmark with connections in the field of Margin. The concepts of her Segmentation study are interwoven with issues in Autonomous agent and Theoretical computer science.

Between 2019 and 2021, her most popular works were:

  • What You See is What You Get: Exploiting Visibility for 3D Object Detection (23 citations)
  • CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning (23 citations)
  • TAO: A Large-Scale Benchmark for Tracking Any Object (12 citations)

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

  • Artificial intelligence
  • Machine learning
  • Computer vision

Her primary areas of study are Artificial intelligence, Computer vision, Object detection, Object and Machine learning. Particularly relevant to Iterative reconstruction is her body of work in Artificial intelligence. Computer vision is often connected to Visualization in her work.

Her Object detection study combines topics from a wide range of disciplines, such as Contextual image classification and Benchmark. Her biological study spans a wide range of topics, including Static image and State. Her Machine learning study combines topics in areas such as Classification methods and Segmentation.

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

Microsoft COCO: Common Objects in Context

Tsung-Yi Lin;Michael Maire;Serge J. Belongie;James Hays.
european conference on computer vision (2014)

11403 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

Microsoft COCO: Common Objects in Context

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

6948 Citations

A discriminatively trained, multiscale, deformable part model

P. Felzenszwalb;D. McAllester;D. Ramanan.
computer vision and pattern recognition (2008)

3069 Citations

Face detection, pose estimation, and landmark localization in the wild

Xiangxin Zhu;Deva Ramanan.
computer vision and pattern recognition (2012)

2556 Citations

Articulated pose estimation with flexible mixtures-of-parts

Yi Yang;Deva Ramanan.
computer vision and pattern recognition (2011)

1229 Citations

Globally-optimal greedy algorithms for tracking a variable number of objects

Hamed Pirsiavash;Deva Ramanan;Charless C. Fowlkes.
computer vision and pattern recognition (2011)

887 Citations

Detecting activities of daily living in first-person camera views

Hamed Pirsiavash;Deva Ramanan.
computer vision and pattern recognition (2012)

736 Citations

Articulated Human Detection with Flexible Mixtures of Parts

Yi Yang;Deva Ramanan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)

670 Citations

Discriminative Models for Multi-Class Object Layout

Chaitanya Desai;Deva Ramanan;Charless C. Fowlkes.
International Journal of Computer Vision (2011)

652 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 Deva Ramanan

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Luc Van Gool

ETH Zurich

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Max Planck Institute for Informatics

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Stanford University

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Chinese University of Hong Kong

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Andrew Zisserman

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University of Oxford

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University of California, Berkeley

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Chunhua Shen

University of Adelaide

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University of California, Berkeley

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Larry S. Davis

University of Maryland, College Park

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Sun Yat-sen University

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Ming-Hsuan Yang

University of California, Merced

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