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UCSD Computer Vision

Piotr Dollár, PhD

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Task Specific Local Region Matching

In this project we trained a distance function for local region matching. We show that when the application is relatively constrained, using a supervised learning approach produces better results than a generic system that was tuned by hand.

Monitoring Animal Behavior

Smart Vivarium: automated monitoring of animal health and welfare.

Locally Smooth Manifold Learning

LSML is a method for determining a warping from a point on a manifold to its neighbors on the manifold. A direct application of this method is performed on video sequences where the ways of moving on the underlying manifold are learned and then used to move within and out of the training set. The wa...

Multiple Component & Pose Learning

This project focuses on visual learning with ambiguity. In particular, we have applied and extended the Multiple Instance Learning (MIL) paradigm to challenging computer vision problems. We propose two novel learning frameworks: Multiple Component Learning (MCL) for part-based object detection, and ...


Dollár P., Appel R., Belongie S., Perona P., "Fast Feature Pyramids for Object Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear, 2014. [BibTex][pdf]
Lin T., Maire M., Belongie S., Hays J., Perona P., Ramanan D., Dollár P., Zitnick L.C., "Microsoft COCO: Common Objects in Context", European Conference on Computer Vision (ECCV), Zürich, September, 2014. [www] [BibTex][pdf]
Dollár P., Belongie S., Perona P., "The Fastest Pedestrian Detector In The West", British Machine Vision Conference (BMVC), Aberystwyth, UK, 2010. [BibTex][pdf]
Dollár P., Tu Z., Perona P., Belongie S., "Integral Channel Features", British Machine Vision Conference (BMVC), London, England, 2009. [BibTex][pdf]
Babenko B., Dollár P., Belongie S., Multiple Instance Learning with Query Bags, , 2009. [BibTex][pdf]
Tu Z., Narr K.L., Dollár P., Dinov I., Thompson P.M., Toga A.W., "Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models", Transactions on Medical Imaging, vol. 27, no. 4, pp. 495-508, April, 2008. [BibTex][pdf]
Dollár P., Babenko B., Perona P., Belongie S., Tu Z., "Multiple Component Learning for Object Detection", European Conference on Computer Vision (ECCV), 2008. [BibTex][pdf]
Babenko B., Dollár P., Tu Z., Belongie S., "Simultaneous Learning and Alignment: Multi-Instance and Multi-Pose Learning", RealFaces, Marseille, France, 2008. [BibTex][pdf]
Dollár P., Rabaud V., Belongie S., "Non-Isometric Manifold Learning: Analysis and an Algorithm", International Conference On Machine Learning (ICML), June, 2007. [BibTex][pdf]
Dollár P., Rabaud V., Belongie S., "Learning to Traverse Image Manifolds", Tech Report, no. CS2007-087: UCSD CSE, 2007. [BibTex][pdf]
Dollár P., Tu Z., Tao H., Belongie S., "Feature Mining For Image Classification", CVPR, 2007. [BibTex][pdf]
Babenko B., Dollár P., Belongie S., "Task Specific Local Region Matching", ICCV, Rio de Janeiro, 2007. [BibTex][pdf]
Dollár P., Rabaud V., Belongie S., "Learning to Traverse Image Manifolds", Neural Information Processing Systems Conference (NIPS), Dec., 2006. [BibTex][pdf]
Dollár P., Tu Z., Belongie S., "Supervised Learning of Edges and Object Boundaries", CVPR, New York City, 2006. [BibTex][pdf]
Belongie S., Branson K., Dollár P., Rabaud V., "Monitoring Animal Behavior in the Smart Vivarium", Measuring Behavior, Wageningen, NL, pp. 70-72, 2005. [BibTex][pdf]
Dollár P., Rabaud V., Cottrell G., Belongie S., "Behavior Recognition via Sparse Spatio-Temporal Features", IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Beijing, China, 2005. [BibTex][pdf]