Temporal sentence grounding involves the retrieval of a video moment with a
natural language query. Many existing works directly incorporate the given
video and temporally localized query for temporal grounding, overlooking the
inherent domain gap between different modalities. In this paper, we utilize
pseudo-query features containing extensive temporally global textual knowledge
sourced from the same video-query pair, to enhance the bridging of domain gaps
and attain a heightened level of similarity between multi-modal features.
Specifically, we propose a Pseudo-query Intermediary Network (PIN) to achieve
an improved alignment of visual and comprehensive pseudo-query features within
the feature space through contrastive learning. Subsequently, we utilize
learnable prompts to encapsulate the knowledge of pseudo-queries, propagating
them into the textual encoder and multi-modal fusion module, further enhancing
the feature alignment between visual and language for better temporal
grounding. Extensive experiments conducted on the Charades-STA and
ActivityNet-Captions datasets demonstrate the effectiveness of our method.

利用伪查询特征加强领域间的联通，提高视觉和语言之间的特征对齐，以实现更好的时间句子定位。