Current approaches for video grounding propose kinds of complex architectures
to capture the video-text relations, and have achieved impressive improvements.
However, it is hard to learn the complicated multi-modal relations by only
architecture designing in fact. In this paper, we introduce a novel Support-set
Based Cross-Supervision (Sscs) module which can improve existing methods during
training phase without extra inference cost. The proposed Sscs module contains
two main components, i.e., discriminative contrastive objective and generative
caption objective. The contrastive objective aims to learn effective
representations by contrastive learning, while the caption objective can train
a powerful video encoder supervised by texts. Due to the co-existence of some
visual entities in both ground-truth and background intervals, i.e., mutual
exclusion, naively contrastive learning is unsuitable to video grounding. We
address the problem by boosting the cross-supervision with the support-set
concept, which collects visual information from the whole video and eliminates
the mutual exclusion of entities. Combined with the original objectives, Sscs
can enhance the abilities of multi-modal relation modeling for existing
approaches. We extensively evaluate Sscs on three challenging datasets, and
show that our method can improve current state-of-the-art methods by large
margins, especially 6.35% in terms of R1@0.5 on Charades-STA.

本文提出了一种支持集合基的跨监督 (Sscs) 模块来改进现有的视频文本关系模型技术，该模块由辨别对比目标和生成描述目标组成，利用 support-set 概念加强互动学习，并在三个具有挑战性的数据集上获得了竞争性的结果。