Causal Visual–Semantic Enhancement for Video-Text Retrieval

The core challenge in video-text retrieval lies in measuring the cross-modal gap between visual and linguistic representations. While mainstream methods leverage deep learning to project video and text features into a shared space for similarity matching, they often rely on superficial statistical correlations. This limits their ability to model underlying causal relationships or capture high-level semantics, resulting in poor interpretability. To address this, we analyze the biases existing in video-text retrieval tasks and introduce a Causal Visual–Semantic Enhancement (CVSE) method that integrates causal inference with deep learning. Our approach applies causal intervention to mitigate bias from contextual confounders and uses textual descriptions as a condition to guide frame aggregation, emphasizing semantically relevant frames while suppressing redundant ones. Experiments on MSR-VTT, MSVD, and LSMDC demonstrate that the proposed method outperforms state-of-the-art retrieval models, validating its superior performance.