The surge in video and social media content underscores the need for a deeper
understanding of multimedia data. Most of the existing mature video
understanding techniques perform well with short formats and content that
requires only shallow understanding, but do not perform well with long format
videos that require deep understanding and reasoning. Deep Video Understanding
(DVU) Challenge aims to push the boundaries of multimodal extraction, fusion,
and analytics to address the problem of holistically analyzing long videos and
extract useful knowledge to solve different types of queries. This paper
introduces a query-aware method for long video localization and relation
discrimination, leveraging an imagelanguage pretrained model. This model
adeptly selects frames pertinent to queries, obviating the need for a complete
movie-level knowledge graph. Our approach achieved first and fourth positions
for two groups of movie-level queries. Sufficient experiments and final
rankings demonstrate its effectiveness and robustness.

本文介绍了一种基于查询的长视频定位和关系判别方法，利用图像语言预训练模型来选择与查询相关的帧，免去了构建完整的电影级知识图谱的需要。该方法在两组电影级查询中取得了第一和第四名的位置，并且经过充分的实验证明其效果和鲁棒性。