Deva K. Ramanan, Ph.D.
Affiliations: | 2005 | University of California, Berkeley, Berkeley, CA, United States |
Area:
Computer Science, Electronics and Electrical EngineeringGoogle:
"Deva Ramanan"Parents
Sign in to add mentor| David Forsyth | grad student | 2005 | UC Berkeley | |
| (Tracking people and recognizing their activities.) | ||||
Children
Sign in to add trainee| Hamed Pirsiavash | grad student | UC Irvine (Computer Science Tree) | |
| Olga Russakovsky | post-doc | 2015-2017 | Carnegie Mellon (Neurotree) |
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Publications
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| Kong B, Supanc̆ic̆ J, Ramanan D, et al. (2019) Cross-Domain Image Matching with Deep Feature Maps International Journal of Computer Vision. 127: 1738-1750 |
| Iă JSS, Rogez G, Yang Y, et al. (2018) Depth-Based Hand Pose Estimation: Methods, Data, and Challenges International Journal of Computer Vision. 126: 1180-1198 |
| Pezzementi Z, Tabor T, Hu P, et al. (2018) Comparing apples and oranges: Off-road pedestrian detection on the National Robotics Engineering Center agricultural person-detection dataset Journal of Field Robotics. 35: 545-563 |
| Gunther M, Hu P, Herrmann C, et al. (2017) Unconstrained Face Detection and Open-Set Face Recognition Challenge International Journal of Central Banking. 697-706 |
| Zhang L, Wang X, Kalashnikov D, et al. (2016) Query-driven Approach to Face Clustering and Tagging. Ieee Transactions On Image Processing : a Publication of the Ieee Signal Processing Society |
| Laptev I, Ramanan D, Sivic J. (2016) Guest Editorial: Video Recognition International Journal of Computer Vision. 119: 217-218 |
| Zhu X, Vondrick C, Fowlkes CC, et al. (2015) Do We Need More Training Data? International Journal of Computer Vision |
| Yang Y, Ramanan D. (2013) Articulated human detection with flexible mixtures of parts. Ieee Transactions On Pattern Analysis and Machine Intelligence. 35: 2878-90 |
| Felzenszwalb P, Girshick R, McAllester D, et al. (2013) Visual object detection with deformable part models Communications of the Acm. 56: 97-105 |
| Vondrick C, Patterson D, Ramanan D. (2013) Efficiently scaling up crowdsourced video annotation: A set of best practices for high quality, economical video labeling International Journal of Computer Vision. 101: 184-204 |