  Text-to-video retrieval (TVR) aims to find the most relevant video in a large
video gallery given a query text. The intricate and abundant context of the
video challenges the performance and efficiency of TVR. To handle the
serialized video contexts, existing methods typically select a subset of frames
within a video to represent the video content for TVR. How to select the most
representative frames is a crucial issue, whereby the selected frames are
required to not only retain the semantic information of the video but also
promote retrieval efficiency by excluding temporally redundant frames. In this
paper, we make the first empirical study of frame selection for TVR. We
systemically classify existing frame selection methods into text-free and
text-guided ones, under which we detailedly analyze six different frame
selections in terms of effectiveness and efficiency. Among them, two frame
selections are first developed in this paper. According to the comprehensive
analysis on multiple TVR benchmarks, we empirically conclude that the TVR with
proper frame selections can significantly improve the retrieval efficiency
without sacrificing the retrieval performance.
