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

TL;DR

The paper tackles data efficiency and cross-modal misalignment in text-video retrieval by adapting CLIP with a frame-difference guided dynamic region perception, implemented as an Alpha-channel input in Alpha-CLIP (FDA-CLIP). It introduces a frame-difference extraction module to produce dynamic region masks, which guide the visual encoder to focus on salient changing regions while suppressing static background information, enabling lightweight aggregation via average pooling. A symmetric cross-entropy loss is used to train the model, and experiments on MSVD and MSR-VTT show improved text-to-video and especially video-to-text retrieval, with robust performance across a range of hyperparameters. The approach achieves competitive or superior results without complex cross-modal fusion modules, and the authors provide code for reproducibility and potential extension to longer sequences and temporal modeling.

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.

Figures (2)

• Figure 1: This figure illustrates the FDA-CLIP framework.
• Figure 2: Sensitivity Verification of Hyperparameter $\tau$.