  This paper presents the method that underlies our submission to the untrimmed
video classification task of ActivityNet Challenge 2016. We follow the basic
pipeline of temporal segment networks and further raise the performance via a
number of other techniques. Specifically, we use the latest deep model
architecture, e.g., ResNet and Inception V3, and introduce new aggregation
schemes (top-k and attention-weighted pooling). Additionally, we incorporate
the audio as a complementary channel, extracting relevant information via a CNN
applied to the spectrograms. With these techniques, we derive an ensemble of
deep models, which, together, attains a high classification accuracy (mAP
$93.23\%$) on the testing set and secured the first place in the challenge.
