H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 85 Citations 41,095 162 World Ranking 339 National Ranking 210

Research.com Recognitions

Awards & Achievements

2020 - IAPR J. K. Aggarwal Prize "For pioneering contributions to unsupervised and self-supervised learning in computer vision and robotics."

2016 - Fellow of Alfred P. Sloan Foundation

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Operating system

Abhinav Gupta mainly focuses on Artificial intelligence, Machine learning, Object detection, Pattern recognition and Object. His Artificial intelligence study frequently links to other fields, such as Computer vision. He combines subjects such as Robot, Pascal and Training set with his study of Machine learning.

His Object detection study combines topics in areas such as Semi-supervised learning, Feature extraction and Knowledge acquisition. The various areas that Abhinav Gupta examines in his Pattern recognition study include Artificial neural network, Surface, Normal and Cluster analysis. His Minimum bounding box study in the realm of Object connects with subjects such as Hollywood.

His most cited work include:

  • Non-local Neural Networks (2476 citations)
  • Unsupervised Visual Representation Learning by Context Prediction (1106 citations)
  • The Stanford Dash multiprocessor (909 citations)

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

His primary areas of investigation include Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Task. His Image, Object, Object detection, Representation and Artificial neural network investigations are all subjects of Artificial intelligence research. His Object detection research incorporates themes from Pascal and Feature learning.

His studies in Machine learning integrate themes in fields like Contextual image classification, Training set and Inference. Many of his studies involve connections with topics such as Visualization and Pattern recognition. His study focuses on the intersection of Task and fields such as Human–computer interaction with connections in the field of Imitation learning and Set.

He most often published in these fields:

  • Artificial intelligence (66.78%)
  • Machine learning (22.03%)
  • Pattern recognition (18.53%)

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

  • Artificial intelligence (66.78%)
  • Human–computer interaction (11.54%)
  • Reinforcement learning (11.19%)

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

His primary scientific interests are in Artificial intelligence, Human–computer interaction, Reinforcement learning, Image and Robot. His research integrates issues of Frame, Structure and Computer vision in his study of Artificial intelligence. His work in Human–computer interaction tackles topics such as Task which are related to areas like Set, Teleoperation and Structure from motion.

His Image research is multidisciplinary, incorporating perspectives in Pixel, Knowledge graph, Object, Variety and Pattern recognition. His Object research is multidisciplinary, relying on both Representation and Information retrieval. His Robot study incorporates themes from Artificial neural network, Imitation, Baseline and Transformer.

Between 2019 and 2021, his most popular works were:

  • Discovering Motor Programs by Recomposing Demonstrations (11 citations)
  • Implicit Mesh Reconstruction from Unannotated Image Collections (9 citations)
  • Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning (7 citations)

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

  • Artificial intelligence
  • Machine learning
  • Operating system

Abhinav Gupta spends much of his time researching Artificial intelligence, Robot, Reinforcement learning, Control and Scaling. His Artificial intelligence study combines topics from a wide range of disciplines, such as Structure, Surface and Pattern recognition. His Robot research integrates issues from Imitation and Structure from motion.

The study incorporates disciplines such as Motor primitives, Theoretical computer science, Limit and Decomposition in addition to Reinforcement learning. His Control research incorporates elements of Machine learning, Latent variable, Frame and Inference. His Scaling research is multidisciplinary, incorporating elements of Margin, Adaptability, Curriculum and Evolutionary learning.

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

Non-local Neural Networks

Xiaolong Wang;Ross Girshick;Abhinav Gupta;Kaiming He.
computer vision and pattern recognition (2018)

1554 Citations

The Visual Object Tracking VOT2016 Challenge Results

Matej Kristan;Aleš Leonardis;Jiři Matas;Michael Felsberg.
european conference on computer vision (2016)

1423 Citations

The Stanford Dash multiprocessor

D. Lenoski;J. Laudon;K. Gharachorloo;W.-D. Weber.
IEEE Computer (1992)

1397 Citations

Ensemble of exemplar-SVMs for object detection and beyond

Tomasz Malisiewicz;Abhinav Gupta;Alexei A. Efros.
international conference on computer vision (2011)

907 Citations

Training Region-Based Object Detectors with Online Hard Example Mining

Abhinav Shrivastava;Abhinav Gupta;Ross Girshick.
computer vision and pattern recognition (2016)

793 Citations

Complete computer system simulation: the SimOS approach

M. Rosenblum;S.A. Herrod;E. Witchel;A. Gupta.
IEEE Parallel & Distributed Technology: Systems & Applications (1995)

727 Citations

Blast Loading and Blast Effects on Structures - An Overview

T. Ngo;P. Mendis;A. Gupta;J. Ramsay.
(2007)

680 Citations

Unsupervised Visual Representation Learning by Context Prediction

Carl Doersch;Abhinav Gupta;Alexei A. Efros.
international conference on computer vision (2015)

662 Citations

Target-driven visual navigation in indoor scenes using deep reinforcement learning

Yuke Zhu;Roozbeh Mottaghi;Eric Kolve;Joseph J. Lim.
international conference on robotics and automation (2017)

645 Citations

Never-ending learning

T. Mitchell;W. Cohen;E. Hruschka;P. Talukdar.
Communications of The ACM (2018)

625 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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