Frame-Difference Guided Dynamic Region Perception for CLIP Adaptation in Text-Video Retrieval

TL;DR

This paper introduces FDA-CLIP, a novel framework that enhances text-video retrieval by focusing on dynamic regions using frame differences, reducing static background redundancy and improving cross-modal alignment.

Contribution

The paper presents a new method that incorporates frame difference-guided dynamic region masks into CLIP, improving dynamic feature extraction for better text-video retrieval performance.

Findings

Effective dynamic region focus improves retrieval accuracy.

Reduces static background redundancy in video features.

Balances retrieval efficiency and accuracy.

Abstract

With the rapid growth of video data, text-video retrieval technology has become increasingly important in numerous application scenarios such as recommendation and search. Early text-video retrieval methods suffer from two critical drawbacks: first, they heavily rely on large-scale annotated video-text pairs, leading to high data acquisition costs; second, there is a significant modal gap between video and text features, which limits cross-modal alignment accuracy. With the development of vision-language model, adapting CLIP to video tasks has attracted great attention. However, existing adaptation methods generally lack enhancement for dynamic video features and fail to effectively suppress static redundant features. To address this issue, this paper proposes FDA-CLIP (Frame Difference Alpha-CLIP), which is a concise CLIP-based training framework for text-video alignment. Specifically, the method uses frame differences to generate dynamic region masks, which are input into Alpha-CLIP as an additional Alpha channel. This proactively guides the model to focus on semantically critical dynamic regions while suppressing static background redundancy. Experiments demonstrate that frame difference-guided video semantic encoding can effectively balance retrieval efficiency and accuracy.