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The surge in video and social media content underscores the need for a deeperunderstanding of multimedia data. Most of the existing mature videounderstanding techniques perform well with short formats and content thatrequires only shallow understanding, but do not perform well with long formatvideos 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 andextract useful knowledge to solve different types of queries. This paperintroduces a query-aware method for long video localization and relationdiscrimination, leveraging an imagelanguage pretrained model. This modeladeptly selects frames pertinent to queries, obviating the need for a completemovie-level knowledge graph. Our approach achieved first and fourth positionsfor two groups of movie-level queries. Sufficient experiments and finalrankings demonstrate its effectiveness and robustness.

Query-aware Long Video Localization and Relation Discrimination for Deep Video Understanding