  We present a Multi-Modal Recipe for Advancing Adaptation-based Pre-training
towards effective and efficient zero-shot video-text retrieval, dubbed M2-RAAP.
Upon popular image-text models like CLIP, most current adaptation-based
video-text pre-training methods are confronted by three major issues, i.e.,
noisy data corpus, time-consuming pre-training, and limited performance gain.
Towards this end, we conduct a comprehensive study including four critical
steps in video-text pre-training. Specifically, we investigate 1) data
filtering and refinement, 2) video input type selection, 3) temporal modeling,
and 4) video feature enhancement. We then summarize this empirical study into
the M2-RAAP recipe, where our technical contributions lie in 1) the data
filtering and text re-writing pipeline resulting in 1M high-quality bilingual
video-text pairs, 2) the replacement of video inputs with key-frames to
accelerate pre-training, and 3) the Auxiliary-Caption-Guided (ACG) strategy to
enhance video features. We conduct extensive experiments by adapting three
image-text foundation models on two refined video-text datasets from different
languages, validating the robustness and reproducibility of M2-RAAP for
adaptation-based pre-training. Results demonstrate that M2-RAAP yields superior
performance with significantly reduced data (-90%) and time consumption (-95%),
establishing a new SOTA on four English zero-shot retrieval datasets and two
Chinese ones. We are preparing our refined bilingual data annotations and
codebase, which will be available at
https://github.com/alipay/Ant-Multi-Modal-Framework/tree/main/prj/M2_RAAP.
