  The correlation between the vision and text is essential for video moment
retrieval (VMR), however, existing methods heavily rely on separate
pre-training feature extractors for visual and textual understanding. Without
sufficient temporal boundary annotations, it is non-trivial to learn universal
video-text alignments. In this work, we explore multi-modal correlations
derived from large-scale image-text data to facilitate generalisable VMR. To
address the limitations of image-text pre-training models on capturing the
video changes, we propose a generic method, referred to as Visual-Dynamic
Injection (VDI), to empower the model's understanding of video moments. Whilst
existing VMR methods are focusing on building temporal-aware video features,
being aware of the text descriptions about the temporal changes is also
critical but originally overlooked in pre-training by matching static images
with sentences. Therefore, we extract visual context and spatial dynamic
information from video frames and explicitly enforce their alignments with the
phrases describing video changes (e.g. verb). By doing so, the potentially
relevant visual and motion patterns in videos are encoded in the corresponding
text embeddings (injected) so to enable more accurate video-text alignments. We
conduct extensive experiments on two VMR benchmark datasets (Charades-STA and
ActivityNet-Captions) and achieve state-of-the-art performances. Especially,
VDI yields notable advantages when being tested on the out-of-distribution
splits where the testing samples involve novel scenes and vocabulary.
