  Current methods for video activity localisation over time assume implicitly
that activity temporal boundaries labelled for model training are determined
and precise. However, in unscripted natural videos, different activities mostly
transit smoothly, so that it is intrinsically ambiguous to determine in
labelling precisely when an activity starts and ends over time. Such
uncertainties in temporal labelling are currently ignored in model training,
resulting in learning mis-matched video-text correlation with poor
generalisation in test. In this work, we solve this problem by introducing
Elastic Moment Bounding (EMB) to accommodate flexible and adaptive activity
temporal boundaries towards modelling universally interpretable video-text
correlation with tolerance to underlying temporal uncertainties in pre-fixed
annotations. Specifically, we construct elastic boundaries adaptively by mining
and discovering frame-wise temporal endpoints that can maximise the alignment
between video segments and query sentences. To enable both more accurate
matching (segment content attention) and more robust localisation (segment
elastic boundaries), we optimise the selection of frame-wise endpoints subject
to segment-wise contents by a novel Guided Attention mechanism. Extensive
experiments on three video activity localisation benchmarks demonstrate
compellingly the EMB's advantages over existing methods without modelling
uncertainty.
