World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
11780
World Ranking
4102
National Ranking
35

Overview

R. Venkatesh Babu is affiliated with the Indian Institute of Science in India and has contributed extensively to the field of computer science, specializing in computer vision and pattern recognition. Their research portfolio includes numerous studies and publications focused on various subfields such as artificial intelligence, radiology and imaging, computational mechanics, and computer graphics.

Their recent notable papers include:

  • Towards Data-Free Model Stealing in a Hard Label Setting, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses, 2020, arXiv (Cornell University)
  • S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • A Closer Look at Smoothness in Domain Adversarial Training, 2022, arXiv (Cornell University)

Their coauthors frequently include Varun Jampani, Sravanti Addepalli, Jogendra Nath Kundu, Harsh Rangwani, and Tejan Karmali. Collaborative work with these researchers has contributed to advancing knowledge in their fields of interest.

R. Venkatesh Babu has published in a range of venues with a high concentration in arXiv (Cornell University), producing 45 works there. Other regular venues include Lecture Notes in Computer Science, the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Munich Personal RePEc Archive, and the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Their research focuses on topics such as:

  • Domain Adaptation and Few-Shot Learning
  • Anomaly Detection Techniques and Applications
  • Adversarial Robustness in Machine Learning
  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Video Surveillance and Tracking Methods
  • Generative Adversarial Networks and Image Synthesis

A book chapter authored by R. Venkatesh Babu is titled "Computer Vision, Pattern Recognition, Image Processing, and Graphics," published by Springer Science+Business Media in 2020.

Best Publications

  • Switching Convolutional Neural Network for Crowd Counting

    Deepak Babu Sam;Shiv Surya;R. Venkatesh Babu

  • DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

    K. Ram Prabhakar;V Sai Srikar;R. Venkatesh Babu

  • CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

    Lokesh Boominathan;Srinivas S S Kruthiventi;R. Venkatesh Babu

  • Data-free parameter pruning for Deep Neural Networks

    Suraj Srinivas;R. Venkatesh Babu

  • DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations

    Srinivas S. S. Kruthiventi;Kumar Ayush;R. Venkatesh Babu

  • DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data

    Swaminathan Gurumurthy;Ravi Kiran Sarvadevabhatla;R. Venkatesh Babu

  • A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

    Suraj Srinivas;Ravi Kiran Sarvadevabhatla;Konda Reddy Mopuri;Nikita Prabhu

  • No-reference image quality assessment using modified extreme learning machine classifier

    S. Suresh;R. Venkatesh Babu;H. J. Kim

  • Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN

    Deepak Babu Sam;Neeraj N Sajjan;R. Venkatesh Babu;Mukundhan Srinivasan

  • Locate, Size, and Count: Accurately Resolving People in Dense Crowds via Detection

    Deepak Babu Sam;Skand Vishwanath Peri;Mukuntha Narayanan Sundararaman;Amogh Kamath

  • Generalizable Data-Free Objective for Crafting Universal Adversarial Perturbations

    Konda Reddy Mopuri;Aditya Ganeshan;R. Venkatesh Babu

  • Universal Source-Free Domain Adaptation

    Jogendra Nath Kundu;Naveen Venkat;Rahul M;R. Venkatesh Babu

  • Training Sparse Neural Networks

    Suraj Srinivas;Akshayvarun Subramanya;R. Venkatesh Babu

  • AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation

    Jogendra Nath Kundu;Phani Krishna Uppala;Anuj Pahuja;R. Venkatesh Babu

  • Robust tracking with motion estimation and local Kernel-based color modeling

    R. Venkatesh Babu;Patrick Pérez;Patrick Bouthemy

  • Saliency Unified: A Deep Architecture for simultaneous Eye Fixation Prediction and Salient Object Segmentation

    Srinivas S. S. Kruthiventi;Vennela Gudisa;Jaley H. Dholakiya;R. Venkatesh Babu

  • Fast Feature Fool: A data independent approach to universal adversarial perturbations

    Konda Reddy Mopuri;Utsav Garg;R. Venkatesh Babu

  • SeamSeg: Video Object Segmentation Using Patch Seams

    S. Avinash Ramakanth;R. Venkatesh Babu

  • Top-Down Feedback for Crowd Counting Convolutional Neural Network.

    Deepak Babu Sam;R. Venkatesh Babu

  • NAG: Network for Adversary Generation

    Konda Reddy Mopuri;Utkarsh Ojha;Utsav Garg;R. Venkatesh Babu

  • Zero-Shot Knowledge Distillation in Deep Networks

    Gaurav Kumar Nayak;Konda Reddy Mopuri;Vaisakh Shaj;R. Venkatesh Babu

  • 3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image

    Priyanka Mandikal;K L Navaneet;Mayank Agarwal;R. Venkatesh Babu

Frequent Co-Authors

Sundaram Suresh
Sundaram Suresh Indian Institute of Science
Patrick Bouthemy
Patrick Bouthemy French Institute for Research in Computer Science and Automation - INRIA
Vishal M. Patel
Vishal M. Patel Johns Hopkins University
Manojit Pramanik
Manojit Pramanik Iowa State University
Hyoung Joong Kim
Hyoung Joong Kim Korea University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens many doors, including flexible online degree options. For those looking to enter the field quickly, the shortest associate degree program pathways can help you gain essential skills and start working sooner. These programs are especially beneficial for career switchers or anyone wanting a fast track into technology careers.

For advanced learners or those seeking leadership roles in education technology, an online edd may be a smart option. Such doctoral-level degrees enable you to specialize further without compromising your current commitments.

Choosing where to study is critical. Many students compare popular online colleges to find accredited institutions with strong reputations and robust student support services.

If you’re passionate about creativity and entertainment, consider looking at game design schools online for degrees that blend technology with innovative storytelling. Online programs provide the flexibility and diversity needed to tailor your computer science education to your career goals.

Best Scientists Citing R. Venkatesh Babu

Trending Scientists

Recently Published Articles