Multi-query Video Retrieval

Zeyu Wang, Yu Wu, Karthik Narasimhan, Olga Russakovsky

Retrieving target videos based on text descriptions is a task of great practical value and has received increasing attention over the past few years. In this paper, we focus on the less-studied setting of multi-query video retrieval, where multiple queries are provided to the model for searching over the video archive. We first show that the multi-query retrieval task is more pragmatic and representative of real-world use cases and better evaluates retrieval capabilities of current models, thereby deserving of further investigation alongside the more prevalent single-query retrieval setup. We then propose several new methods for leveraging multiple queries at training time to improve over simply combining similarity outputs of multiple queries from regular single-query trained models. Our models consistently outperform several competitive baselines over three different datasets. For instance, Recall@1 can be improved by 4.7 points on MSR-VTT, 4.1 points on MSVD and 11.7 points on VATEX over a strong baseline built on the state-of-the-art CLIP4Clip model. We believe further modeling efforts will bring new insights to this direction and spark new systems that perform better in real-world video retrieval applications. Code is available at https://github.com/princetonvisualai/MQVR.

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