  The key of the text-to-video retrieval (TVR) task lies in learning the unique
similarity between each pair of text (consisting of words) and video
(consisting of audio and image frames) representations. However, some problems
exist in the representation alignment of video and text, such as a text, and
further each word, are of different importance for video frames. Besides, audio
usually carries additional or critical information for TVR in the case that
frames carry little valid information. Therefore, in TVR task,
multi-granularity representation of text, including whole sentence and every
word, and the modal of audio are salutary which are underutilized in most
existing works. To address this, we propose a novel multi-granularity feature
interaction module called MGFI, consisting of text-frame and word-frame, for
video-text representations alignment. Moreover, we introduce a cross-modal
feature interaction module of audio and text called CMFI to solve the problem
of insufficient expression of frames in the video. Experiments on benchmark
datasets such as MSR-VTT, MSVD, DiDeMo show that the proposed method
outperforms the existing state-of-the-art methods.
