  Retrieving target videos based on text descriptions is a task of great
practical value and has received increasing attention over the past few years.
Despite recent progress, imperfect annotations in existing video retrieval
datasets have posed significant challenges on model evaluation and development.
In this paper, we tackle this issue by focusing on the less-studied setting of
multi-query video retrieval, where multiple descriptions are provided to the
model for searching over the video archive. We first show that multi-query
retrieval task effectively mitigates the dataset noise introduced by imperfect
annotations and better correlates with human judgement on evaluating retrieval
abilities of current models. We then investigate several methods which leverage
multiple queries at training time, and demonstrate that the multi-query
inspired training can lead to superior performance and better generalization.
We hope further investigation in this direction can bring new insights on
building systems that perform better in real-world video retrieval
applications.
