  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.
