Entity disambiguation resolves the many to many correspondence between mentions of entities in text and unique real-world entities. Our entity disambiguation uses language-independent entity context to agglomeratively resolve mentions with similar names to unique entities. This paper describes our automatic entity disambiguation capability and assesses its performance on the second Web People S...

Recently, many methods of person re-identification (ReID) rely on part-based feature representation to learn a discriminative pedestrian descriptor. However, the spatial context between these parts is ignored for the independent extractor on each separate part. In this paper, we propose to apply Long Short-Term Memory (LSTM) in an end-to-end way to model the pedestrian, seen as a sequence of bo...

State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single vector of global weights, which are assumed to be universally good for all individuals, independent to their different appearances. In this study, we show that certain features play more important role than others unde...