In text-video retrieval, recent works have benefited from the powerful
learning capabilities of pre-trained text-image foundation models (e.g., CLIP)
by adapting them to the video domain. A critical problem for them is how to
effectively capture the rich semantics inside the video using the image encoder
of CLIP. To tackle this, state-of-the-art methods adopt complex cross-modal
modeling techniques to fuse the text information into video frame
representations, which, however, incurs severe efficiency issues in large-scale
retrieval systems as the video representations must be recomputed online for
every text query. In this paper, we discard this problematic cross-modal fusion
process and aim to learn semantically-enhanced representations purely from the
video, so that the video representations can be computed offline and reused for
different texts. Concretely, we first introduce a spatial-temporal "Prompt
Cube" into the CLIP image encoder and iteratively switch it within the encoder
layers to efficiently incorporate the global video semantics into frame
representations. We then propose to apply an auxiliary video captioning
objective to train the frame representations, which facilitates the learning of
detailed video semantics by providing fine-grained guidance in the semantic
space. With a naive temporal fusion strategy (i.e., mean-pooling) on the
enhanced frame representations, we obtain state-of-the-art performances on
three benchmark datasets, i.e., MSR-VTT, MSVD, and LSMDC.

该研究论文介绍了一种在文本 - 视频检索中学习视频语义表示的方法，通过将一个空间 - 时间上下文模块引入图像编码器，并通过辅助视频字幕目标进行训练，以提高视频帧的语义能力。在增强后的帧表示上使用简单的时序融合策略，取得了三个基准数据集（MSR-VTT，MSVD 和 LSMDC）的最先进性能。