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Lab-based teaching in which hands-on experiments are to be conducted by students takes an important part for a wide range of engineering and science disciplines. In our current practice, the lab-based teaching involves live demonstration and tutorials after the off-line lab manual review. This has become particularly problematic when the number of students is large and insufficiency on the lab-supporting...
In many classification problems, there exists additional information which is available during training but not available during testing. In this paper we denote such information as hidden information, and study how to incorporate it to improve the learning performance. Despite its importance, learning with hidden information has not attracted enough attention from the field and existing work in this...
The purpose of this paper is to develop an approach to learn dynamic Bayesian network (DBN) discriminatively for human activity recognition. DBN is a generative model widely used for modeling temporal events in human activity recognition. The parameters of the DBN models are usually learned through maximizing likelihood or expected likelihood. However, activity is often recognized through identifying...
This paper proposes a novel probabilistic approach to utilize clip attributes as hidden knowledge for event recognition. Event recognition in surveillance videos is very challenging due to its large intra-class variations and relative low image resolution. The clip attributes, that are available only during training, provide auxiliary hidden information about the variation of the event appearance...
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