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Computer Science

D-Index
90
Citations
95805
World Ranking
590
National Ranking
316

Overview

Deva Ramanan is affiliated with Carnegie Mellon University in the United States. Their research primarily spans the fields of Computer Science and Engineering, with a focus on several subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Computational Mechanics, and Aerospace Engineering.

The scientist's work covers a range of specialized topics such as Advanced Neural Network Applications, Advanced Vision and Imaging, Domain Adaptation and Few-Shot Learning, Advanced Image and Video Retrieval Techniques, Computer Graphics and Visualization Techniques, 3D Shape Modeling and Analysis, and Autonomous Vehicle Technology and Safety.

Deva Ramanan has contributed to numerous papers in various publication venues. The more frequent venues for their publications include:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • IEEE Robotics and Automation Letters

Some of their recent papers are:

  • RU-AI: A Large Multimodal Dataset for Machine Generated Content Detection, 2024, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Depth-supervised NeRF: Fewer Views and Faster Training for Free, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Long-Tailed Recognition via Weight Balancing, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting, 2023, arXiv (Cornell University)

Deva Ramanan frequently collaborates with several coauthors, including Neehar Peri, Shu Kong, James Hays, Achal Dave, and Tarasha Khurana.

Best Publications

  • Microsoft COCO: Common Objects in Context

    Tsung-Yi Lin;Michael Maire;Serge J. Belongie;James Hays

  • Object Detection with Discriminatively Trained Part-Based Models

    P F Felzenszwalb;R B Girshick;D McAllester;D Ramanan

  • A discriminatively trained, multiscale, deformable part model

    P. Felzenszwalb;D. McAllester;D. Ramanan

  • Microsoft COCO: Common Objects in Context

    Tsung-Yi Lin;Michael Maire;Serge Belongie;Lubomir Bourdev

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

    Xiangxin Zhu;Deva Ramanan

  • Articulated pose estimation with flexible mixtures-of-parts

    Yi Yang;Deva Ramanan

  • Argoverse: 3D Tracking and Forecasting With Rich Maps

    Ming-Fang Chang;Deva Ramanan;James Hays;John Lambert

  • Depth-supervised NeRF: Fewer Views and Faster Training for Free

    Unknown

  • Articulated Human Detection with Flexible Mixtures of Parts

    Yi Yang;Deva Ramanan

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

    Hamed Pirsiavash;Deva Ramanan;Charless C. Fowlkes

  • A large-scale benchmark dataset for event recognition in surveillance video

    Sangmin Oh;Anthony Hoogs;Amitha Perera;Naresh Cuntoor

  • Detecting activities of daily living in first-person camera views

    Hamed Pirsiavash;Deva Ramanan

  • Finding Tiny Faces

    Peiyun Hu;Deva Ramanan

  • Discriminative Models for Multi-Class Object Layout

    Chaitanya Desai;Deva Ramanan;Charless C. Fowlkes

  • Efficiently Scaling up Crowdsourced Video Annotation

    Carl Vondrick;Donald Patterson;Deva Ramanan

  • 3D Human Pose Estimation = 2D Pose Estimation + Matching

    Ching-Hang Chen;Deva Ramanan

  • Learning to parse images of articulated bodies

    Deva Ramanan

  • ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification

    Rohit Girdhar;Rohit Girdhar;Deva Ramanan;Abhinav Gupta;Josef Sivic

  • Need for Speed: A Benchmark for Higher Frame Rate Object Tracking

    Hamed Kiani Galoogahi;Ashton Fagg;Chen Huang;Deva Ramanan

  • Strike a pose: tracking people by finding stylized poses

    D. Ramanan;D.A. Forsyth;A. Zisserman

  • Learning to Model the Tail

    Yu-Xiong Wang;Deva Ramanan;Martial Hebert

  • Argoverse: 3D Tracking and Forecasting with Rich Maps

    Ming-Fang Chang;John Lambert;Patsorn Sangkloy;Jagjeet Singh

Frequent Co-Authors

Charless C. Fowlkes
Charless C. Fowlkes University of California, Irvine
Yaser Sheikh
Yaser Sheikh Facebook (United States)
David Forsyth
David Forsyth University of Illinois at Urbana-Champaign
Carl Vondrick
Carl Vondrick Columbia University
Hamed Pirsiavash
Hamed Pirsiavash University of California, Davis
Simon Lucey
Simon Lucey University of Adelaide
Simon Baker
Simon Baker Microsoft (United States)
Martial Hebert
Martial Hebert Carnegie Mellon University
David McAllester
David McAllester Toyota Technological Institute at Chicago
Abhinav Gupta
Abhinav Gupta Carnegie Mellon University

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