Deva Ramanan

Professor Carnegie Mellon University

  • Pittsburgh PA

Deva Ramanan's research interests span computer vision and machine learning, with a focus on visual recognition.

Contact

Carnegie Mellon University

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Biography

Deva Ramanan is a professor in the Robotics Institute at Carnegie Mellon University and the director of the CMU Argo AI Center for Autonomous Vehicle Research. The Center engages in fundamental research to produce advanced perception and next-generation decision-making algorithms that enable vehicles to perceive and navigate autonomously in diverse real-world urban conditions. His research interests span computer vision and machine learning, with a focus on visual recognition often motivated by the task of understanding people from visual data. He served at the program chair of the IEEE Computer Vision and Pattern Recognition (CVPR) 2018. He is on the editorial board of the International Journal of Computer Vision and is an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence. He regularly serves as a senior program committee member for CVPR, the International Conference on Computer Vision, and the European Conference on Computer Vision. He also regularly serves on NSF panels for computer vision and machine learning.

Areas of Expertise

Human-Centered Robotics
Human-Robot Collaboration
Machine Learning Embedded in Systems
Neurorobotics
3-D Vision and Recognition
Computer Vision
Visual Servoing and Visual Tracking
First-Person Vision
Sensing & Perception
Graphics & Creative Tools

Media Appearances

SpreeAI Is Redefining Retail With Virtual AI-Powered Try-Ons Curated by the Top in Tech and Fashion

Associated Press  online

2025-05-06

Deva Ramanan (Robotics Institute) believes SpreeAI is "assembling a team that understands both the deep technical challenges and their product impact." SpreeAI is an app that allows users to virtually try on outfits.

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Generative modeling tool renders 2D sketches in 3D

Tech Xplore  online

2023-04-06

"As long as you can draw a sketch, you can make your own customized 3D model," said RI doctoral candidate Kangle Deng, who was part of the research team with Zhu, Professor Deva Ramanan and Ph.D. student Gengshan Yang.

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Self-driving cars would be nowhere without HD maps

Axios  online

2021-08-09

"Even though a traffic light and the moon may resemble each other, a self-driving system should use a combination of contextual cues — including spatial, temporal and prior knowledge — to tell them apart," Deva Ramanan, principal scientist at self-driving tech competitor Argo AI explains in a blog post.

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Industry Expertise

Computer Networking
Automotive

Accomplishments

IARPA Award for "Walk-Through Rendering From Images of Varying Altitudes

2023-2027

Education

University of California at Berkeley

Ph.D.

Electrical Engineering and Computer Science

University of Delaware

B.S.

Computer Engineering

Affiliations

  • IEEE Computer Vision and Pattern Recognition (CVPR)
  • International Journal of Computer Vision
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Articles

Edge-based Privacy-Sensitive Live Learning for Discovery of Training Data

Proceedings of the 1st International Workshop on Networked AI Systems

2023

Finding true positives (TPs) to construct a training set for a new class of interest in machine learning (ML) is often a challenge. The novelty of the class suggests that cloud archives are unlikely to be helpful. We observe that most video data collected for surveillance and briefly stored at the edge before being overwritten is currently unused. To efficiently harness this untapped resource, we describe Delphi, a privacy-sensitive interactive labeling system that continuously improves labeling productivity through background learning. Our experimental results confirm the value of Delphi for training set construction from edge-sourced data.

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Towards long-tailed 3d detection

Conference on Robot Learning

2023

Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for training 3D detectors, particularly on large-scale lidar data. Surprisingly, although semantic class labels naturally follow a long-tailed distribution, contemporary benchmarks focus on only a few common classes (eg, pedestrian and car) and neglect many rare classes in-the-tail (eg, debris and stroller). However, AVs must still detect rare classes to ensure safe operation. Moreover, semantic classes are often organized within a hierarchy, eg, tail classes such as child and construction-worker are arguably subclasses of pedestrian. However, such hierarchical relationships are often ignored, which may lead to misleading estimates of performance and missed opportunities for algorithmic innovation.

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WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

2023

The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E GEneration): a synthetic dataset generated with a vision-language generative model via prompting. WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection. We have analyzed WEDGE from research standpoints, verifying its effectiveness for extreme-weather autonomous perception. We establish baseline performance for classification and detection with 53.87% test accuracy and 45.41 mAP.

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