  Most existing text-video retrieval methods focus on cross-modal matching
between the visual content of videos and textual query sentences. However, in
real-world scenarios, online videos are often accompanied by relevant text
information such as titles, tags, and even subtitles, which can be utilized to
match textual queries. This insight has motivated us to propose a novel
approach to text-video retrieval, where we directly generate associated
captions from videos using zero-shot video captioning with knowledge from
web-scale pre-trained models (e.g., CLIP and GPT-2). Given the generated
captions, a natural question arises: what benefits do they bring to text-video
retrieval? To answer this, we introduce Cap4Video, a new framework that
leverages captions in three ways: i) Input data: video-caption pairs can
augment the training data. ii) Intermediate feature interaction: we perform
cross-modal feature interaction between the video and caption to produce
enhanced video representations. iii) Output score: the Query-Caption matching
branch can complement the original Query-Video matching branch for text-video
retrieval. We conduct comprehensive ablation studies to demonstrate the
effectiveness of our approach. Without any post-processing, Cap4Video achieves
state-of-the-art performance on four standard text-video retrieval benchmarks:
MSR-VTT (51.4%), VATEX (66.6%), MSVD (51.8%), and DiDeMo (52.0%). The code is
available at https://github.com/whwu95/Cap4Video .

Computer Vision and Pattern Recognition