视频文本跨模态检索研究综述OA北大核心CSTPCD

Modalities define the specific forms in which data exist.The swift expansion of various modal data types has brought multimodal learning into the limelight.As a crucial subset of this field,cross-modal retrieval has achieved noteworthy advancements,particularly in integrating images and text.However,videos,as opposed to images,encapsulate a richer array of modal data and offer a more extensive spectrum of information.This rich-ness aligns well with the growing user demand for comprehensive and adaptable information retrieval solutions.Consequently,video-text cross-modal retrieval has emerged as a burgeoning area of research in recent times.To thor-oughly comprehend video-text cross-modal retrieval and its state-of-the-art developments,a methodical review and summarization of the existing representative methods is conducted.Initially,the focus is on analyzing current deep learning-based unidirectional and bidirectional video-text cross-modal retrieval methods.This analysis includes an in-depth exploration of seminal works within each category,highlighting their strengths and weaknesses.Subse-quently,the discussion shifts to an experimental viewpoint,introducing benchmark datasets and evaluation met-rics specific to video-text cross-modal retrieval.The performance of several standard methods in benchmark data-sets is compared.Finally,the application prospects and future research challenges of video-text cross-modal retrieval are discussed.