Learning to learn implicit queries from gaze patterns
Proceedings of the 25th international conference on Machine learning
Max-margin Classification of Data with Absent Features
The Journal of Machine Learning Research
Learning to Recognize Activities from the Wrong View Point
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Shape-Based Object Localization for Descriptive Classification
International Journal of Computer Vision
Probabilistic Models of Object Geometry with Application to Grasping
International Journal of Robotics Research
From Images to Shape Models for Object Detection
International Journal of Computer Vision
FastInf: An Efficient Approximate Inference Library
The Journal of Machine Learning Research
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We consider the important challenge of recognizing a variety of deformable object classes in images. Of fundamental importance and particular difficulty in this setting is the problem of "outlining" an object, rather than simply deciding on its presence or absence. A major obstacle in learning a model that will allow us to address this task is the need for hand-segmented training images. In this paper we present a novel landmark-based, piecewise-linear model of the shape of an object class. We then formulate a learning approach that allows us to learn this model with minimal user supervision. We circumvent the need for hand-segmentation by transferring the shape "essence" of an object from drawings to complex images. We show that our method is able to automatically and effectively learn and localize a variety of object classes.