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

• URL: http://arxiv.org/abs/2104.08860v1
• Date: Sun, 18 Apr 2021 13:59:50 GMT
• Title: CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
• Authors: Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, Tianrui Li
• Abstract summary: The CLIP (Contrastive Language-Image Pre-training) has demonstrated the power of visual concepts learning from web collected image-text datasets. We propose a CLIP4Clip model to transfer the knowledge of the CLIP model to video-language retrieval in an end-to-end manner.
• Score: 31.7091206926183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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