Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval
Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, Tat‐Seng Chua
Abstract
The rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradigm on retrieval with simple queries, which are usually ineffective for complex queries that carry far more complex semantics. Recently, embedding-based paradigm has emerged as a popular approach. It aims to map the queries and videos into a shared embedding space where semantically-similar texts and videos are much closer to each other. Despite its simplicity, it forgoes the exploitation of the syntactic structure of text queries, making it suboptimal to model the complex queries.
Topics & Concepts
Computer scienceEmbeddingInformation retrievalSemantics (computer science)Encoding (memory)Simple (philosophy)ModalQuery expansionThe InternetWorld Wide WebArtificial intelligenceChemistryPhilosophyProgramming languageEpistemologyPolymer chemistryMultimodal Machine Learning ApplicationsAdvanced Image and Video Retrieval TechniquesVideo Analysis and Summarization