The paper proposes DiscoVLA to improve video-text retrieval using CLIP by addressing vision, language, and alignment discrepancies, achieving superior performance.
The parameter-efficient adaptation of the image-text pretraining model CLIP
for video-text retrieval is a prominent area of research. While CLIP is focused
on image-level vision-language matching, video-text retrieval demands
comprehensive understanding at the video level. Three key discrepancies emerge
in the transfer from image-level to video-level: vision, language, and
alignment. However, existing methods mainly focus on vision while neglecting
language and alignment. In this paper, we propose Discrepancy Reduction in
Vision, Language, and Alignment (DiscoVLA), which simultaneously mitigates all
three discrepancies. Specifically, we introduce Image-Video Features Fusion to
integrate image-level and video-level features, effectively tackling both
vision and language discrepancies. Additionally, we generate pseudo image
captions to learn fine-grained image-level alignment. To mitigate alignment
discrepancies, we propose Image-to-Video Alignment Distillation, which
leverages image-level alignment knowledge to enhance video-level alignment.
Extensive experiments demonstrate the superiority of our DiscoVLA. In
particular, on MSRVTT with CLIP (ViT-B/16), DiscoVLA outperforms previous
methods by 1.5% in R@1, reaching a final score of 50.5% R@1. The code is
available at https://github.com/LunarShen/DsicoVLA.
