  This paper introduces a fast and efficient network architecture, NeXtVLAD, to
aggregate frame-level features into a compact feature vector for large-scale
video classification. Briefly speaking, the basic idea is to decompose a
high-dimensional feature into a group of relatively low-dimensional vectors
with attention before applying NetVLAD aggregation over time. This NeXtVLAD
approach turns out to be both effective and parameter efficient in aggregating
temporal information. In the 2nd Youtube-8M video understanding challenge, a
single NeXtVLAD model with less than 80M parameters achieves a GAP score of
0.87846 in private leaderboard. A mixture of 3 NeXtVLAD models results in
0.88722, which is ranked 3rd over 394 teams. The code is publicly available at
https://github.com/linrongc/youtube-8m.
