  Text-video retrieval is a critical multi-modal task to find the most relevant
video for a text query. Although pretrained models like CLIP have demonstrated
impressive potential in this area, the rising cost of fully finetuning these
models due to increasing model size continues to pose a problem. To address
this challenge, prompt tuning has emerged as an alternative. However, existing
works still face two problems when adapting pretrained image-text models to
downstream video-text tasks: (1) The visual encoder could only encode
frame-level features and failed to extract global-level general video
information. (2) Equipping the visual and text encoder with separated prompts
failed to mitigate the visual-text modality gap. To this end, we propose DGL, a
cross-modal Dynamic prompt tuning method with Global-Local video attention. In
contrast to previous prompt tuning methods, we employ the shared latent space
to generate local-level text and frame prompts that encourage inter-modal
interaction. Furthermore, we propose modeling video in a global-local attention
mechanism to capture global video information from the perspective of prompt
tuning. Extensive experiments reveal that when only 0.67% parameters are tuned,
our cross-modal prompt tuning strategy DGL outperforms or is comparable to
fully finetuning methods on MSR-VTT, VATEX, LSMDC, and ActivityNet datasets.
Code will be available at https://github.com/knightyxp/DGL
