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35 Results

June 27, 2016

Unsupervised Learning of Edges


Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object boundaries.

By: Yin Li, Manohar Paluri, James M. Rehg, Piotr Dollar
June 18, 2016

Learning Physical Intuition of Block Towers by Example

International Conference on Machine Learning

Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world. In this paper, we explore the ability of deep feed-forward models to learn such intuitive physics.

By: Adam Lerer, Sam Gross, Rob Fergus
May 2, 2016

Metric Learning with Adaptive Density Discrimination


Distance metric learning approaches learn a transformation to a representation space in which distance is in correspondence with a predefined notion of similarity.

By: Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev
December 15, 2015

Learning to Segment Object Candidates


In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model obtains substantially higher object recall using fewer proposals. We also show that our model is able to generalize to unseen categories it has not seen during training.

By: Pedro Oliveira, Ronan Collobert, Piotr Dollar
February 17, 2015

What Makes for Effective Detection Proposals?


An in depth study of object proposals and their effect on object detection performance.

By: Bernt Schiele, Jan Hosang, Piotr Dollar, Rodrigo Benenson