  Temporal localization remains an important challenge in video understanding.
In this work, we present our solution to the 3rd YouTube-8M Video Understanding
Challenge organized by Google Research. Participants were required to build a
segment-level classifier using a large-scale training data set with noisy
video-level labels and a relatively small-scale validation data set with
accurate segment-level labels. We formulated the problem as a multiple instance
multi-label learning and developed an attention-based mechanism to selectively
emphasize the important frames by attention weights. The model performance is
further improved by constructing multiple sets of attention networks. We
further fine-tuned the model using the segment-level data set. Our final model
consists of an ensemble of attention/multi-attention networks, deep bag of
frames models, recurrent neural networks and convolutional neural networks. It
ranked 13th on the private leader board and stands out for its efficient usage
of resources.
