  Modern video-text retrieval frameworks basically consist of three parts:
video encoder, text encoder and the similarity head. With the success on both
visual and textual representation learning, transformer based encoders and
fusion methods have also been adopted in the field of video-text retrieval. In
this report, we present CLIP2TV, aiming at exploring where the critical
elements lie in transformer based methods. To achieve this, We first revisit
some recent works on multi-modal learning, then introduce some techniques into
video-text retrieval, finally evaluate them through extensive experiments in
different configurations. Notably, CLIP2TV achieves 52.9@R1 on MSR-VTT dataset,
outperforming the previous SOTA result by 4.1%.
