  Temporal sentence grounding aims to detect the event timestamps described by
the natural language query from given untrimmed videos. The existing
fully-supervised setting achieves great performance but requires expensive
annotation costs; while the weakly-supervised setting adopts cheap labels but
performs poorly. To pursue high performance with less annotation cost, this
paper introduces an intermediate partially-supervised setting, i.e., only
short-clip or even single-frame labels are available during training. To take
full advantage of partial labels, we propose a novel quadruple constraint
pipeline to comprehensively shape event-query aligned representations, covering
intra- and inter-samples, uni- and multi-modalities. The former raises
intra-cluster compactness and inter-cluster separability; while the latter
enables event-background separation and event-query gather. To achieve more
powerful performance with explicit grounding optimization, we further introduce
a partial-full union framework, i.e., bridging with an additional
fully-supervised branch, to enjoy its impressive grounding bonus, and be robust
to partial annotations. Extensive experiments and ablations on Charades-STA and
ActivityNet Captions demonstrate the significance of partial supervision and
our superior performance.
