  This paper focuses on the temporal aspect for recognizing human activities in
videos; an important visual cue that has long been undervalued. We revisit the
conventional definition of activity and restrict it to Complex Action: a set of
one-actions with a weak temporal pattern that serves a specific purpose.
Related works use spatiotemporal 3D convolutions with fixed kernel size, too
rigid to capture the varieties in temporal extents of complex actions, and too
short for long-range temporal modeling. In contrast, we use multi-scale
temporal convolutions, and we reduce the complexity of 3D convolutions. The
outcome is Timeception convolution layers, which reasons about minute-long
temporal patterns, a factor of 8 longer than best related works. As a result,
Timeception achieves impressive accuracy in recognizing the human activities of
Charades, Breakfast Actions, and MultiTHUMOS. Further, we demonstrate that
Timeception learns long-range temporal dependencies and tolerate temporal
extents of complex actions.
