Chaitanya Desai
A computer vision researcher, Chaitanya Desai co-wrote a paper on multi-class object layout. The paper, written with Deva Ramanan and Charless Fowlkes, won the ICCV Marr Prize in 2009. He completed a doctorate at the University of California, Irvine in 2012, with a dissertation on relational models for human-object interactions. In 2012 he and Ramanan also published a paper on detecting actions, poses and objects at the European Conference on Computer Vision. Between 2015 and 2016, he co-wrote papers on CNN features for generative classification and on unsupervised underwater fish detection.
- Works
- 10
- Citations
- 808
- h-index
- 6
Publication numbers from OpenAlex, as of 15 October 2026.
Timeline
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2009
The ICCV Marr Prize
He co-writes "Discriminative Models for Multi-Class Object Layout" with Deva Ramanan and Charless Fowlkes, and that paper wins the ICCV Marr Prize in 2009.
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October 2011
A journal article
In October 2011, the same paper appears as a journal article by Chaitanya Desai, Deva Ramanan and Charless Fowlkes.
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2012
A doctorate at UC Irvine
He completes a Ph.D. at the University of California, Irvine in 2012, with a dissertation on relational models for human-object interactions.
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2012
Detecting actions and poses
In 2012, he and Deva Ramanan publish a paper on detecting actions, poses and objects at the European Conference on Computer Vision.
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2015
CNN features for generative classification
In 2015, he helps write "De-Correlating CNN Features for Generative Classification" for the Winter Conference on Applications of Computer Vision.
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2016
Unsupervised fish detection
In 2016, he co-writes a workshop paper on unsupervised underwater fish detection for the Winter Applications of Computer Vision Workshops.
Sources and credits
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- Discriminative Models for Multi-Class Object Layout | ML Anthology
- Chaitanya Desai - Wikidata
- Discriminative Models for Multi-Class Object Layout - Robotics Institute Carnegie Mellon University
- Chaitanya Desai - The Mathematics Genealogy Project
- Detecting Actions, Poses, and Objects with Relational Phraselets | ML Anthology
- De-Correlating CNN Features for Generative Classification | ML Anthology
- Unsupervised Underwater Fish Detection Fusing Flow and Objectiveness | ML Anthology