A Straightforward Framework For Video Retrieval Using CLIP
This work addresses video retrieval for users needing efficient matching without annotations, but it is incremental as it extends an existing model to a new domain.
The authors tackled video retrieval by applying the CLIP language-image model to obtain video representations without user annotations, achieving state-of-the-art results on MSR-VTT and MSVD benchmarks.
Video Retrieval is a challenging task where a text query is matched to a video or vice versa. Most of the existing approaches for addressing such a problem rely on annotations made by the users. Although simple, this approach is not always feasible in practice. In this work, we explore the application of the language-image model, CLIP, to obtain video representations without the need for said annotations. This model was explicitly trained to learn a common space where images and text can be compared. Using various techniques described in this document, we extended its application to videos, obtaining state-of-the-art results on the MSR-VTT and MSVD benchmarks.