Cuiwei Liu

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In cross-view action recognition, what you saw in one view is different from what you recognize in another view, since the data distribution even the feature space can change from one view to another. In this paper, we address the problem of transferring action models learned in one view (source view) to another different view (target view), where action(More)
A novel transfer learning approach, referred to as Transfer Discriminant-Analysis of Canonical Correlations (Transfer DCC), is proposed to recognize human actions from one view (target view) via the discriminative model learned from another view (source view). To cope with the considerable change between feature distributions of source view and target view,(More)
This paper addresses the challenging problem of complex human activity understanding from long videos. Towards this goal, we propose a hierarchical description of an activity video, referring to the “which” of activities, “what” of atomic actions, and “when” of atomic actions happening in the video. In our work, each complex activity is characterized as a(More)
This paper addresses the problem of joint recognition and localization of actions in videos. We develop a novel Transfer Latent Support Vector Machine (TLSVM) by using Web images and weakly annotated training videos. In order to alleviate the laborious and time-consuming manual annotations of action locations, the model takes training videos which are only(More)
Most of the existing action recognition approaches employ low-level features(e.g., local features and global features) to represent an action video. However, algorithms based on low-level features are not robust to complex environments such as cluttered background, camera movement and illumination change. In this paper , we present a novel random forest(More)
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