A formal bio is here
My research focuses on computer vision, often motivated by the task of understanding people from visual data. My work tends to make heavy use of machine learning techniques, often using the human visual system as inspiration. For example, temporal processing is a key component of human perception, but is still relatively unexploited in current visual recognition systems. Machine learning from big (visual) data allows systems to learn subtle statistical regularities of the visual world. But humans have the ability to learn from very few examples. Here's a talk
(from 2015) that discusses some thoughts on these issues.
I am currently a principle scientist at Argo AI and the director of the CMU Argo AI Center for Autonomous Vehicle Research
. We have several post-doctoral and research staff positions. Please contact me if you are interested.
Current students and postdoctoral fellows
- Masters / undergraduate
- Sean Cha (joint with Aswin Sankaranarayanan)
- Aaron Huang (joint with John Dolan)
- Jessica Lee
- Tarasha Khurana
- Krishna Uppala (joint with Aswin Sankaranarayanan)
- Haochen Wang
- William Qi
Past students and postdoctoral fellows
- Postdoctoral fellow
- Rohit Girdhar Learning to Understand People via Local, Global, and Temporal Reasoning, 2019, Facebook AI
- Phuc Nguyen Visual Recognition with Limited Annotations, 2018, Google
- James Supancic Long-Term Tracking by Decision-Making, 2017, Blizzard
- Mohsen Hejrati Recognizing and Reconstructing Objects in 3D, 2015, Genentech
- Dennis Park Tracking People and Their Poses, 2014, Toyota Research Institute
- Xiangxin Zhu Sharing Information Across Object Templates, 2014, Google
- Yi Yang Articulated Human Pose Estimation with Mixtures of Parts, 2013, DeepMind
- Chaitanya Desai Relational Models for Human-Object Interactions and their Affordances, 2012, Amazon
- Hamed Pirsiavash Scalable Action Recognition in Continuous Video Streams, 2012, UMBC
- Siva Mynepalli Recognizing Tiny Faces, 2019, Nimble Robotics
- Ishan Nigam Learning with Auxillary Supervision, 2019, UT Austin
- Vivek Krishnan Tinkering under the Hood: Interactive Zero-Shot Learning with Net Surgery, 2016, Microsoft
- Carl Vondrick Crowdsourcing Video Annotation, 2011, Columbia
- Goutham Patnaik A Joint Model for Tracking and Recognizing Human Actions in Video, 2009, Google
- Program Chair, CVPR 2018
- Editorial Board, IJCV
- Associate Editor, IEEE TPAMI
- DARPA Award for "Agile Dimorphic Execution for Dispersed Computing" (2017-2019).
- Intel Science and Tech. Center for Visual Cloud Systems (2016-present).
- NSF Award for "Probabilistic Hierarchical Models for Multi-Task Visual Recognition" (2016-2018).
- IARPA Award for "Sparse Heterogeneous Representations and
Domain Adaptive Matching for Unconstrained Face Recognition" (2014-2018).
- NIST Award for "Center for Statistics and Applications in Forensic Evidence" (2015-2019)
- NSF Award for "The Intelligent Workcell - Enabling Robots and People to Work Together Safely in Manufacturing Environments" (2012-2017).
For a complete list, please see my Google Scholar
For pre-prints, please see my ArXiv
For older work, please see here
- M. Li, Y. Wang, D. Ramanan. Towards Streaming Image Understanding, ECCV 2020.
- A. Dave, T. Khurana, P. Tokmakov, C. Schmid, D. Ramanan. TAO: A Large-scale Benchmark for Tracking Any Object, ECCV 2020.
- J. Zhang, S. Pepose, H. Joo, D. Ramanan, J. Malik, A. Kanazawa. Perceiving 3D Human-Object SpatialArrangements from a Single Image in the Wild, ECCV 2020.
- G. Yang, D. Ramanan. Upgrading Optical Flow to 3D Scene Flow through Optical Expansion, CVPR 2020.
- P. Hu, D. Held, D. Ramanan. What You See Is What You Get: Exploiting Visibility for 3D Object Detection, CVPR 2020.
- A. Bansal, M. Vo, Y. Sheikh, D. Ramanan, S. Narasimhan. 4D Visualization of Dynamic Events from
Unconstrained Multi-View Videos. CVPR 2020.
- P. Hu, D. Held*, D. Ramanan*. Learning to Optimally Segment Point Clouds, IEEE Robotics and Automation Letters (RA-L), 2020.
- R. Girdhar, D. Ramanan. CATER: A Diagnostic Dataset for Compositional Actions & TEmporal Reasoning, ICLR 2020.
- W. Qi, R. Mullapudi, S. Gupta, D. Ramanan. Learning to Move with Affordance Maps, ICLR 2020.
- M. Li, E. Yumer, D. Ramanan. Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints, ICLR 2020.
- J. Lee, D. Ramanan, R. Girdhar. MetaPix: Few-Shot Video Retargeting, ICLR 2020.
- G. Yang, D. Ramanan. Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019.
- R. Mullapudi, S. Chen, K. Zhang, D. Ramanan, K. Fatahalian. Online Model Distillation for Efficient Video Inference, ICCV 2019.
- Y. Wang, D. Ramanan, M. Hebert. Meta-Learning to Detect Rare Objects, ICCV 2019.
- R. Gridhar, D. Tran, L. Torresani, D. Ramanan. DistInit: Learning Video Representations without a Single Labeled Video, ICCV 2019.
- I. Nigam, P. Tokmakov, D. Ramanan. Towards Latent Attribute Discovery from Triplet Similarities, ICCV 2019.
- P. Nguyen, D. Ramanan, C. Fowlkes. Weakly-supervised Action Localization with Background Modeling, ICCV 2019.
- G. Yang, P. Hu, D. Ramanan. Inferring Distributions Over Depth From a Single Image, IROS 2019.
- A. Bansal, Y. Sheikh. D. Ramanan. Shapes and Context: In-the-Wild Image Synthesis & Manipulation, CVPR 2019. (Best Paper Award Finalist).
- G. Yang, J. Manela, M. Happold, D. Ramanan. Hierarchical Deep Stereo Matching on High Resolution Images, CVPR 2019.
- M. Chang, J. Lambert, P. Sangkloy, S. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, J. Hayes. Argoverse: 3D Tracking and Forecasting with Rich Maps, CVPR 2019.
- P. Hu, Z. Lipton, A. Anandkumar, D. Ramanan. Active Learning with Partial Feedback, ICLR 2019.
- M. Li, Z. Lin, R. Mech, E. Yumer, D. Ramanan. Photo-Sketching: Inferring Contour Drawings from Images , WACV 2019.
- A. Bansal, S. Ma, D. Ramanan, Y. Sheikh. Recycle-GAN: Unsupervised Video Retargeting, ECCV 2018.
- L. Gui, Y. Wang, D. Ramanan, J. Moura. Few-Shot Human Motion Prediction via Meta-Learning, ECCV 2018.
- P. Nguyen, D. Ramanan, C. Fowlkes. Active Testing, ICML 2018.
- A. Bansal, Y. Sheikh, D. Ramanan. PixelNN: Example-based Image Synthesis, ICLR 2018.
- M. Li, L. Jeni, D. Ramanan. Brute-Force Facial Landmark Analysis With A 140,000-Way Classifier, AAAI 2018.
- Hang, R. Murphy, D. Ramanan. Learning Generative Models of Tissue Organization wih Supervised GANs, WACV 2018.
- J. Wang, O. Russakovsky, D. Ramanan. The More You Look, the More You See: Towards General Object Understanding Through Recursive Refinement, WACV 2018.
- I. Nigam, C. Huang, D. Ramanan. Ensemble Knowledge Transfer for Semantic Segmentation, WACV 2018.
- R. Girdhar, D. Ramanan. Attentional Pooling for Action Recognition, NeurIPS 2017.
- Y. Wang, D. Ramanan, M. Hebert. Learning to Model the Tail, NeurIPS 2017.
- B. Kong, J. Supancic, D. Ramanan, C. Fowlkes. Cross-Domain Forensic Shoeprint Matching, BMVC 2017. (Best Industrial Paper, Honorable Mention).
- Z. Pezzementi, T. Tabor, P. Hu, J. Chang, D. Ramanan, C. Wellington, B. Babu, H. Herman. Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset, Journal of Feild Robotics 2017.
- M. Gunther et al. Unconstrained Face Detection and Open-Set Face Recognition Challenge, IJCB 2017.
- J. Supancic, D. Ramanan. Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning, ICCV 2017.
- C. Huang, S. Lucey, D. Ramanan. Learning Policies for Adaptive Tracking with Deep Feature Cascades, ICCV 2017.
- H. Galoogahi, A. Fagg, C. Huang, D. Ramanan, S. Lucey. Need for Speed: A Benchmark for Higher Frame Rate Object Tracking, ICCV 2017.
- P. Hu, D. Ramanan. Finding Tiny Faces, CVPR 2017.
- C. Chen, D. Ramanan. 3D Human Pose Estimation = 2D Pose Estimation + Matching, CVPR 2017.
- S. Huang, D. Ramanan. Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters, CVPR 2017.
- A. Dave, O. Russakovsky, D. Ramanan. Predictive-Corrective Networks for Action Detection, CVPR 2017.
- R. Girdhar, D. Ramanan, A. Gupta, J. Sivic, B. Russell. ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification, CVPR 2017.
- Y. Wang, D. Ramanan, M. Hebert. Growing a Brain: Fine-Tuning by Increasing Model Capacity, CVPR 2017.