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.

翻译：时序句子定位旨在从给定的未修剪视频中检测自然语言查询描述的事件时间戳。现有的全监督设置取得了出色性能，但需要昂贵的标注成本；而弱监督设置采用廉价标签但效果不佳。为了以更少标注成本追求高性能，本文引入一种中间部分监督设置，即训练过程中仅提供短片段甚至单帧标签可用。为了充分利用部分标签，我们提出了一种新颖的四重约束流程，全面塑造事件-查询对齐表示，涵盖样本内/样本间、单模态/多模态维度。前者提升簇内紧凑性和簇间可分离性；后者实现事件-背景分离与事件-查询聚合。为了实现更强大的显式定位优化性能，我们进一步引入部分-全联合框架，即通过额外全监督分支进行桥接，以享受其令人印象深刻的定位增益，并对部分标注具有鲁棒性。在Charades-STA和ActivityNet Captions上的大量实验和消融研究表明了部分监督的重要性及我们方法的优越性能。