In this paper, we investigate how deviation in evaluation activities may reveal bias on the part of reviewers and controversy on the part of evaluated objects. We focus on a `data...
We address the learning of trust based on past observations and context information. We argue that from the truster's point of view trust is best expressed as one of several ...
We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of mode...
We present a discriminative shape-based algorithm for object category localization and recognition. Our method learns object models in a weakly-supervised fashion, without requiri...
Marius Leordeanu, Martial Hebert, Rahul Sukthankar
The bottleneck in interactive visual classification is the exchange of information between human and machine. We introduce the concept of the visible model, which is an ion of an ...