  Temporal action detection in long videos is an important problem.
State-of-the-art methods address this problem by applying action classifiers on
sliding windows. Although sliding windows may contain an identifiable portion
of the actions, they may not necessarily cover the entire action instance,
which would lead to inferior performance. We adapt a two-stage temporal action
detection pipeline with Cascaded Boundary Regression (CBR) model.
Class-agnostic proposals and specific actions are detected respectively in the
first and the second stage. CBR uses temporal coordinate regression to refine
the temporal boundaries of the sliding windows. The salient aspect of the
refinement process is that, inside each stage, the temporal boundaries are
adjusted in a cascaded way by feeding the refined windows back to the system
for further boundary refinement. We test CBR on THUMOS-14 and TVSeries, and
achieve state-of-the-art performance on both datasets. The performance gain is
especially remarkable under high IoU thresholds, e.g<EMAIL_ADDRESS>on THUMOS-14
is improved from 19.0% to 31.0%.
