  When recognizing a long-range activity, exploring the entire video is
exhaustive and computationally expensive, as it can span up to a few minutes.
Thus, it is of great importance to sample only the salient parts of the video.
We propose TimeGate, along with a novel conditional gating module, for sampling
the most representative segments from the long-range activity. TimeGate has two
novelties that address the shortcomings of previous sampling methods, as
SCSampler. First, it enables a differentiable sampling of segments. Thus,
TimeGate can be fitted with modern CNNs and trained end-to-end as a single and
unified model.Second, the sampling is conditioned on both the segments and
their context. Consequently, TimeGate is better suited for long-range
activities, where the importance of a segment heavily depends on the video
context.TimeGate reduces the computation of existing CNNs on three benchmarks
for long-range activities: Charades, Breakfast and MultiThumos. In particular,
TimeGate reduces the computation of I3D by 50% while maintaining the
classification accuracy.
