Abstract We present an active learning framework that predicts the tradeoff between the effort and information gain associated with a candidate image annotation, thereby ranking un...
Conditional random fields (CRFs) have been quite successful in various machine learning tasks. However, as larger and larger data become acceptable for the current computational ma...
As natural language understanding research advances towards deeper knowledge modeling, the tasks become more and more complex: we are interested in more nuanced word characteristi...
Radu Florian, Hongyan Jing, Nanda Kambhatla, Imed ...
Abstract. One solution to the lack of label problem is to exploit transfer learning, whereby one acquires knowledge from source-domains to improve the learning performance in the t...
Human-nameable visual “attributes” can benefit various recognition tasks. However, existing techniques restrict these properties to categorical labels (for example, a person ...