Early weakly supervised video grounding (WSVG) methods often struggle with
incomplete boundary detection due to the absence of temporal boundary
annotations. To bridge the gap between video-level and boundary-level
annotation, explicit-supervision methods, i.e., generating pseudo-temporal
boundaries for training, have achieved great success. However, data
augmentations in these methods might disrupt critical temporal information,
yielding poor pseudo boundaries. In this paper, we propose a new perspective
that maintains the integrity of the original temporal content while introducing
more valuable information for expanding the incomplete boundaries. To this end,
we propose EtC (Expand then Clarify), first use the additional information to
expand the initial incomplete pseudo boundaries, and subsequently refine these
expanded ones to achieve precise boundaries. Motivated by video continuity,
i.e., visual similarity across adjacent frames, we use powerful multimodal
large language models (MLLMs) to annotate each frame within initial pseudo
boundaries, yielding more comprehensive descriptions for expanded boundaries.
To further clarify the noise of expanded boundaries, we combine mutual learning
with a tailored proposal-level contrastive objective to use a learnable
approach to harmonize a balance between incomplete yet clean (initial) and
comprehensive yet noisy (expanded) boundaries for more precise ones.
Experiments demonstrate the superiority of our method on two challenging WSVG
datasets.

通过扩展初始伪边界并精炼扩展边界，结合多模态大型语言模型、互相学习和定制的对比学习目标，我们的方法在两个具有挑战性的 WSVG 数据集上表现出优越性。