Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome fo...
Anelia Angelova, Yaser S. Abu-Mostafa, Pietro Pero...
Many tracking methods face a fundamental dilemma in practice: tracking has to be computationally efficient but verifying if or not the tracker is following the true target tends t...
Traditional image retrieval methods require a "query image" to initiate a search for members of an image category. However, when the image database is unstructured, and ...
The paper addresses the problem of improving the MPEG compression of synthetic video sequences by exploiting the knowledge about the original 3D model. Two techniques are proposed...
The standard model of supervised learning assumes that training and test data are drawn from the same underlying distribution. This paper explores an application in which a second...