Changes

Boosting techniques have been applied to various computer vision tasks, however, most of them are used for classification purposes. In this talk, I will first present Boosted Regression Tree (BRT), a regression version of the boosting used with regression trees, and its simple extension to multidimensional output regression. Then I will show the applications of the BRT on three computer vision tasks, head pose estimation, human pose estimation and class-specific object shape estimation. The BRT is applied to the head pose estimation task straightforwardly. In the human pose estimation task, a body pose estimation task is divided into a set of local pose estimation tasks that maintain a dependency structure and the BRT is used to solve those local pose estimation tasks in a successive manner. Lastly, the class-specific object shape estimation task is addressed by introducing a low dimensional space representing the object shape manifold and using it to bridge the image feature space and the original object shape space.

Boosting techniques have been applied to various computer vision tasks, however, most of them are used for classification purposes. In this talk, I will first present Boosted Regression Tree (BRT), a regression version of the boosting used with regression trees, and its simple extension to multidimensional output regression. Then I will show the applications of the BRT on three computer vision tasks, head pose estimation, human pose estimation and class-specific object shape estimation. The BRT is applied to the head pose estimation task straightforwardly. In the human pose estimation task, a body pose estimation task is divided into a set of local pose estimation tasks that maintain a dependency structure and the BRT is used to solve those local pose estimation tasks in a successive manner. Lastly, the class-specific object shape estimation task is addressed by introducing a low dimensional space representing the object shape manifold and using it to bridge the image feature space and the original object shape space.

===Attributes for Classifier Feedback===

===Attributes for Classifier Feedback===

Speaker: [http://www.umiacs.umd.edu/~arijit/ Prof.  Arijit Biswas] : March 14, 2013

Speaker: [http://www.umiacs.umd.edu/~arijit/ Arijit Biswas] : March 14, 2013

Active learning provides useful tools to reduce annotation costs without compromising classifier performance. However it traditionally views the supervisor simply as a labeling machine. Recently a new interactive learning paradigm was introduced that allows the supervisor to additionally convey useful domain knowledge using attributes. The learner first conveys its belief about an actively chosen image e.g. "I think this is a forest, what do you think?''. If the learner is wrong, the supervisor provides an explanation e.g. "No, this is too open to be a forest''. With access to a pre-trained set of relative attribute predictors, the learner fetches all unlabeled images more open than the query image, and uses them as negative examples of forests to update its classifier. This rich human-machine communication leads to better classification performance. In this talk, we talk about three improvements over this set-up. First, we incorporate a weighting scheme that instead of making a hard decision reasons about the likelihood of an image being a negative example. Second, we do away with pre-trained attributes and instead learn the attribute models on the fly, alleviating overhead and restrictions of a pre-determined attribute vocabulary. Finally, we propose an active learning framework that accounts for not just the label- but also the attributes-based feedback while selecting the next query image. We demonstrate significant improvement in classification accuracy on faces and shoes. We also collect and make available the largest relative attributes dataset containing 29 attributes of faces from 60 categories.

Active learning provides useful tools to reduce annotation costs without compromising classifier performance. However it traditionally views the supervisor simply as a labeling machine. Recently a new interactive learning paradigm was introduced that allows the supervisor to additionally convey useful domain knowledge using attributes. The learner first conveys its belief about an actively chosen image e.g. "I think this is a forest, what do you think?''. If the learner is wrong, the supervisor provides an explanation e.g. "No, this is too open to be a forest''. With access to a pre-trained set of relative attribute predictors, the learner fetches all unlabeled images more open than the query image, and uses them as negative examples of forests to update its classifier. This rich human-machine communication leads to better classification performance. In this talk, we talk about three improvements over this set-up. First, we incorporate a weighting scheme that instead of making a hard decision reasons about the likelihood of an image being a negative example. Second, we do away with pre-trained attributes and instead learn the attribute models on the fly, alleviating overhead and restrictions of a pre-determined attribute vocabulary. Finally, we propose an active learning framework that accounts for not just the label- but also the attributes-based feedback while selecting the next query image. We demonstrate significant improvement in classification accuracy on faces and shoes. We also collect and make available the largest relative attributes dataset containing 29 attributes of faces from 60 categories.