关键词: 多模态, 跨模态检索, 深度学习, 特征提取

Abstract: 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 richness 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 thoroughly 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. Subsequently, the discussion shifts to an experimental viewpoint, introducing benchmark datasets and evaluation metrics specific to video-text cross-modal retrieval. The performance of several standard methods in benchmark datasets is compared. Finally, the application prospects and future research challenges of video- text cross-modal retrieval are discussed.

Key words: multi-modality, cross-modal retrieval, deep learning, feature extraction