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CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval

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arxiv 2104.08860 v2 pith:NLXDZ2HY submitted 2021-04-18 cs.CV

Huaishao Luo , Lei Ji , Ming Zhong , Yang Chen , Wen Lei , Nan Duan , Tianrui Li This is my paper · ORCID
classification cs.CV
keywords clipmodelretrievalvideo-textclip4clipdatasetsempiricalpre-training
verification ladder T0 review T1 audit T2 compute T3 formal
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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, LSMDC, ActivityNet, and DiDeMo. We release our code at https://github.com/ArrowLuo/CLIP4Clip.

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Cited by 13 Pith papers

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