  Moments capture a huge part of our lives. Accurate recognition of these
moments is challenging due to the diverse and complex interpretation of the
moments. Action recognition refers to the act of classifying the desired
action/activity present in a given video. In this work, we perform experiments
on Moments in Time dataset to recognize accurately activities occurring in 3
second clips. We use state of the art techniques for visual, auditory and
spatio temporal localization and develop method to accurately classify the
activity in the Moments in Time dataset. Our novel approach of using Visual
Based Textual features and fusion techniques performs well providing an overall
89.23 % Top - 5 accuracy on the 20 classes - a significant improvement over the
Baseline TRN model.
