Deep Learning for Video-Text Retrieval: a Review

TL;DR

This review paper summarizes over 100 studies on deep learning methods for Video-Text Retrieval, highlighting recent progress, challenges like feature learning and cross-modal gap, and suggesting future research directions.

Contribution

It provides a comprehensive overview of recent deep learning approaches in VTR, benchmarks state-of-the-art performance, and discusses future challenges and directions.

Findings

Deep learning significantly advances VTR performance.

Efficient spatial-temporal feature learning remains challenging.

Cross-modal gap reduction is crucial for improved retrieval accuracy.

Abstract

Video-Text Retrieval (VTR) aims to search for the most relevant video related to the semantics in a given sentence, and vice versa. In general, this retrieval task is composed of four successive steps: video and textual feature representation extraction, feature embedding and matching, and objective functions. In the last, a list of samples retrieved from the dataset is ranked based on their matching similarities to the query. In recent years, significant and flourishing progress has been achieved by deep learning techniques, however, VTR is still a challenging task due to the problems like how to learn an efficient spatial-temporal video feature and how to narrow the cross-modal gap. In this survey, we review and summarize over 100 research papers related to VTR, demonstrate state-of-the-art performance on several commonly benchmarked datasets, and discuss potential challenges and directions, with the expectation to provide some insights for researchers in the field of video-text retrieval.