Video moment retrieval (VMR) is to search for a visual temporal moment in an
untrimmed raw video by a given text query description (sentence). Existing
studies either start from collecting exhaustive frame-wise annotations on the
temporal boundary of target moments (fully-supervised), or learn with only the
video-level video-text pairing labels (weakly-supervised). The former is poor
in generalisation to unknown concepts and/or novel scenes due to restricted
dataset scale and diversity under expensive annotation costs; the latter is
subject to visual-textual mis-correlations from incomplete labels. In this
work, we introduce a new approach called hybrid-learning video moment retrieval
to solve the problem by knowledge transfer through adapting the video-text
matching relationships learned from a fully-supervised source domain to a
weakly-labelled target domain when they do not share a common label space. Our
aim is to explore shared universal knowledge between the two domains in order
to improve model learning in the weakly-labelled target domain. Specifically,
we introduce a multiplE branch Video-text Alignment model (EVA) that performs
cross-modal (visual-textual) matching information sharing and multi-modal
feature alignment to optimise domain-invariant visual and textual features as
well as per-task discriminative joint video-text representations. Experiments
show EVA's effectiveness in exploring temporal segment annotations in a source
domain to help learn video moment retrieval without temporal labels in a target
domain.

用混合学习的方法，从全监督的源领域迁移到弱标记的目标领域，提高视频时刻检索模型的学习能力。通过引入多支视觉文本对齐模型 (EVA)，实现跨模态匹配信息共享和多模态特征对齐，优化领域不变的视觉和文本特征，以及具有判别性的联合视频和文本表示。实验证明 EVA 在源领域的时间段注释中的有效性，可以帮助在目标领域中学习无时间标签的视频时刻检索。