Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

This paper introduces a state-of-the-art video representation and applies it to efficient action
recognition and detection. We first propose to improve the popular dense trajectory features …

The need for early detection of temporal events from sequential data arises in a wide
spectrum of applications ranging from human-robot interaction to video security. While …

Action recognition in uncontrolled video is an important and challenging computer vision
problem. Recent progress in this area is due to new local features and models that capture …

This paper proposes efficient and powerful deep networks for action prediction from partially
observed videos containing temporally incomplete action executions. Different from after-the …

Research on event-based processing and analysis of media is receiving an increasing
attention from the scientific community due to its relevance for an abundance of applications …

Attribute-based representation has shown great promises for visual recognition due to its
intuitive interpretation and cross-category generalization property. However, human efforts …

A considerable portion of web images capture events that occur in our personal lives or
social activities. In this paper, we aim to develop an effective method for recognizing events …

The speed with which intelligent systems can react to an action depends on how soon it can
be recognized. The ability to recognize ongoing actions is critical in many applications, for …

This paper proposes a new video representation for few-example event recognition and
translation. Different from existing representations, which rely on either low-level features, or …