Siamese Learning with Joint Alignment and Regression for
  Weakly-Supervised Video Paragraph Grounding
Chaolei Tan Jianhuang Lai Wei-Shi Zheng Jian-Fang Hu
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.