  Multimedia event detection has been receiving increasing attention in recent
years. Besides recognizing an event, the discovery of evidences (which is
refered to as "recounting") is also crucial for user to better understand the
searching result. Due to the difficulty of evidence annotation, only limited
supervision of event labels are available for training a recounting model. To
deal with the problem, we propose a weakly supervised evidence discovery method
based on self-paced learning framework, which follows a learning process from
easy "evidences" to gradually more complex ones, and simultaneously exploit
more and more positive evidence samples from numerous weakly annotated video
segments. Moreover, to evaluate our method quantitatively, we also propose two
metrics, \textit{PctOverlap} and \textit{F1-score}, for measuring the
performance of evidence localization specifically. The experiments are
conducted on a subset of TRECVID MED dataset and demonstrate the promising
results obtained by our method.
