Video Paragraph Grounding (VPG) is an emerging task in video-language
understanding, which aims at localizing multiple sentences with semantic
relations and temporal order from an untrimmed video. However, existing VPG
approaches are heavily reliant on a considerable number of temporal labels that
are laborious and time-consuming to acquire. In this work, we introduce and
explore Weakly-Supervised Video Paragraph Grounding (WSVPG) to eliminate the
need of temporal annotations. Different from previous weakly-supervised
grounding frameworks based on multiple instance learning or reconstruction
learning for two-stage candidate ranking, we propose a novel siamese learning
framework that jointly learns the cross-modal feature alignment and temporal
coordinate regression without timestamp labels to achieve concise one-stage
localization for WSVPG. Specifically, we devise a Siamese Grounding TRansformer
(SiamGTR) consisting of two weight-sharing branches for learning complementary
supervision. An Augmentation Branch is utilized for directly regressing the
temporal boundaries of a complete paragraph within a pseudo video, and an
Inference Branch is designed to capture the order-guided feature correspondence
for localizing multiple sentences in a normal video. We demonstrate by
extensive experiments that our paradigm has superior practicability and
flexibility to achieve efficient weakly-supervised or semi-supervised learning,
outperforming state-of-the-art methods trained with the same or stronger
supervision.

视频段落定位是视频语言理解中的新兴任务，其旨在从未修剪的视频中定位具有语义关系和时间顺序的多个句子。本研究提出并探索了弱监督视频段落定位，消除了对耗时且繁重的时间标签的需求。通过引入新颖的暹罗学习框架，我们联合学习跨模态特征对齐和无需时间戳标签的时间坐标回归，实现基于一阶定位的简洁弱监督视频段落定位。我们通过广泛实验验证了我们的范例具有卓越的实用性和灵活性，能够以高效的弱监督或半监督学习实现，并在使用相同或更强监督训练的最先进方法上表现出色。