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We present CLIP2Video network to transfer the image-language pre-trainingmodel to video-text retrieval in an end-to-end manner. Leading approaches inthe domain of video-and-language learning try to distill the spatio-temporalvideo features and multi-modal interaction between videos and languages from alarge-scale video-text dataset. Different from them, we leverage pretrainedimage-language model, simplify it as a two-stage framework with co-learning ofimage-text and enhancing temporal relations between video frames and video-textrespectively, make it able to train on comparatively small datasets.Specifically, based on the spatial semantics captured by ContrastiveLanguage-Image Pretraining (CLIP) model, our model involves a TemporalDifference Block to capture motions at fine temporal video frames, and aTemporal Alignment Block to re-align the tokens of video clips and phrases andenhance the multi-modal correlation. We conduct thorough ablation studies, andachieve state-of-the-art performance on major text-to-video and video-to-textretrieval benchmarks, including new records of retrieval accuracy on MSR-VTT,MSVD and VATEX.

CLIP2Video: Mastering Video-Text Retrieval via Image CLIP