Concept Language Models and Event-based Concept Number Selection for Zero-example Event Detection

<p>Zero-example event detection is a problem where, given an event query as input but no example videos for training a detector, the system retrieves the most closely related videos. In this paper we present a fully-automatic zero-example event detection method that is based on translating the event description to a predefined set of concepts for which previously trained visual concept detectors are available. We adopt the use of Concept Language Models (CLMs), which is a method of augmenting semantic concept definition, and we propose a new concept-selection method for deciding on the appropriate number of the concepts needed to describe an event query. The proposed system achieves state-of-the-art performance in automatic zero-example event detection.</p

ITI - CERTH in TRECVID 2016 Ad - hoc Video Search (AVS)

<p>This presentation provides an overview of the runs submitted to TRECVID 2016 by ITI-CERTH in the Ad-hoc Video Search (AVS) task. Our AVS task participation is based on a method that combines the linguistic analysis of the query and the concept-based annotation of video fragments.</p