Multimodal video retrieval with CLIP: a user study

Abstract

Recent machine learning advances demonstrate the effectiveness of zero-shot models trained on large amounts of data collected from the internet. Among these, CLIP (Contrastive Language-Image Pre-training) has been introduced as a multimodal model with high accuracy on a number of different tasks and domains. However, the unconstrained nature of the model begs the question whether it can be deployed in open-domain real-word applications effectively in front of non-technical users. In this paper, we evaluate whether CLIP can be used for multimodal video retrieval in a real-world environment. For this purpose, we implemented impa, an efficient shot-based retrieval system powered by CLIP. We additionally implemented advanced query functionality in a unified graphical user interface to facilitate an intuitive and efficient usage of CLIP for video retrieval tasks. Finally, we empirically evaluated our retrieval system by performing a user study with video editing professionals and journalists working in the TV news media industry. After having the participants solve open-domain video retrieval tasks, we collected data via questionnaires, interviews, and UI interaction logs. Our evaluation focused on the perceived intuitiveness of retrieval using natural language, retrieval accuracy, and how users interacted with the system’s UI. We found that our advanced features yield higher task accuracy, user ratings, and more efficient queries. Overall, our results show the importance of designing intuitive and efficient user interfaces to be able to deploy large models such as CLIP effectively in real-world scenarios.