  Video-text retrieval plays an essential role in multi-modal research and has
been widely used in many real-world web applications. The CLIP (Contrastive
Language-Image Pre-training), an image-language pre-training model, has
demonstrated the power of visual concepts learning from web collected
image-text datasets. In this paper, we propose a CLIP4Clip model to transfer
the knowledge of the CLIP model to video-language retrieval in an end-to-end
manner. Several questions are investigated via empirical studies: 1) Whether
image feature is enough for video-text retrieval? 2) How a post-pretraining on
a large-scale video-text dataset based on the CLIP affect the performance? 3)
What is the practical mechanism to model temporal dependency between video
frames? And 4) The Hyper-parameters sensitivity of the model on video-text
retrieval task. Extensive experimental results present that the CLIP4Clip model
transferred from the CLIP can achieve SOTA results on various video-text
retrieval datasets, including MSR-VTT, MSVC, and LSMDC.
