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arXiv · 2302.09850

Constraint and Union for Partially-Supervised Temporal Sentence Grounding

Chen Ju·Haicheng Wang·Jinxiang Liu·Chaofan Ma·Ya Zhang·Peisen Zhao·Jianlong Chang·Qi Tian
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Abstract

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.

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Chen Ju, Haicheng Wang, Jinxiang Liu, Chaofan Ma, Ya Zhang, Peisen Zhao, Jianlong Chang, Qi Tian. 2023-02-20. Constraint and Union for Partially-Supervised Temporal Sentence Grounding. https://arxiv.org/abs/2302.09850

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