Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a person’s activities and signiď¬...
We present a novel hierarchical prior structure for supervised transfer learning in named entity recognition, motivated by the common structure of feature spaces for this task acr...
Estimating mode (walking/running/standing) and phases of human locomotion is important for video understanding. We present a new ”tracking as recognition” approach. A hierarch...
We describe a new hierarchical representation for twodimensional objects that captures shape information at multiple levels of resolution. This representation is based on a hierar...
We present a biologically motivated architecture for object recognition that is based on a hierarchical feature-detection model in combination with a memory architecture that impl...