CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment
This work addresses video-language representation alignment for researchers and practitioners in computer vision and natural language processing, offering an incremental improvement by adapting existing models to a new domain.
The paper tackled the problem of adapting pre-trained image-text models like CLIP to video-language tasks, finding that data scale and domain gaps hinder performance, and proposed CLIP-ViP, which improves video-text retrieval by a large margin and achieves state-of-the-art results on datasets such as MSR-VTT, DiDeMo, LSMDC, and ActivityNet.
The pre-trained image-text models, like CLIP, have demonstrated the strong power of vision-language representation learned from a large scale of web-collected image-text data. In light of the well-learned visual features, some existing works transfer image representation to video domain and achieve good results. However, how to utilize image-language pre-trained model (e.g., CLIP) for video-language pre-training (post-pretraining) is still under explored. In this paper, we investigate two questions: 1) what are the factors hindering post-pretraining CLIP to further improve the performance on video-language tasks? and 2) how to mitigate the impact of these factors? Through a series of comparative experiments and analyses, we find that the data scale and domain gap between language sources have great impacts. Motivated by these, we propose a Omnisource Cross-modal Learning method equipped with a Video Proxy mechanism on the basis of CLIP, namely CLIP-ViP. Extensive results show that our approach improves the performance of CLIP on video-text retrieval by a large margin. Our model also achieves SOTA results on a variety of datasets, including MSR-VTT, DiDeMo, LSMDC, and ActivityNet. We will release our code and pre-trained CLIP-ViP models at https://github.com/microsoft/XPretrain/tree/main/CLIP-ViP.