Timeception for Complex Action Recognition

This paper focuses on the temporal aspect for recognizing human activities in videos; an important visual cue that has long been either disregarded or ill-used. We revisit the conventional definition of an 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 human activities of Charades. Further, we conduct analysis to demonstrate that Timeception learns long-range temporal dependencies and tolerate temporal extents of complex actions.