Accurate video moment retrieval (VMR) requires universal visual-textual
correlations that can handle unknown vocabulary and unseen scenes. However, the
learned correlations are likely either biased when derived from a limited
amount of moment-text data which is hard to scale up because of the prohibitive
annotation cost (fully-supervised), or unreliable when only the video-text
pairwise relationships are available without fine-grained temporal annotations
(weakly-supervised). Recently, the vision-language models (VLM) demonstrate a
new transfer learning paradigm to benefit different vision tasks through the
universal visual-textual correlations derived from large-scale vision-language
pairwise web data, which has also shown benefits to VMR by fine-tuning in the
target domains. In this work, we propose a zero-shot method for adapting
generalisable visual-textual priors from arbitrary VLM to facilitate
moment-text alignment, without the need for accessing the VMR data. To this
end, we devise a conditional feature refinement module to generate
boundary-aware visual features conditioned on text queries to enable better
moment boundary understanding. Additionally, we design a bottom-up proposal
generation strategy that mitigates the impact of domain discrepancies and
breaks down complex-query retrieval tasks into individual action retrievals,
thereby maximizing the benefits of VLM. Extensive experiments conducted on
three VMR benchmark datasets demonstrate the notable performance advantages of
our zero-shot algorithm, especially in the novel-word and novel-location
out-of-distribution setups.

我们提出了一种零样本方法，可以从任意的视觉语言模型中获得可泛化的视觉文字先验，并利用条件特征细化模块和自下而上的提案生成策略来改善视频片段与文本的对齐，从而在视频片段检索中实现显著的性能优势。