摘要：本文提出分布对齐桥（Distribution-Alignment Bridge，DAB）框架，将文本到视频检索重新定义为分布对齐任务，而非传统的确定性点匹配。DAB 将文本和视频嵌入均建模为由均值和方差定义的高斯分布，从而显式考虑模态特有的不确定性。我们采用一种受扩散模型启发的确定性桥，通过截断式细化过程，迭代地将文本分布细化至其目标视频分布。该方法将概率嵌入与分布变换统一为一个连贯的端到端可训练系统。为优化跨模态相似度，我们引入了基于 Kullback-Leibler 散度的分布感知对比损失。在 MSR-VTT、MSVD 和 VATEX 基准上的广泛评估证实，DAB 显著优于现有的概率式和基于扩散的基线方法，同时还能通过桥诱导的分布边际，提供经过校准的不确定性感知排序。

Abstract:This paper proposes the Distribution-Alignment Bridge (DAB), a framework that reconceptualizes text-to-video retrieval as a distribution alignment task rather than traditional deterministic point matching. By modeling both text and video embeddings as Gaussian distributions defined by mean and variance, DAB explicitly accounts for modality-specific uncertainty. We employ a deterministic, diffusion-inspired bridge to iteratively refine text distributions toward their target video distributions through a truncated refinement process. This approach unifies probabilistic embedding and distributional transformation into a cohesive, end-to-end trainable system. To optimize cross-modal similarity, we introduce a distribution-aware contrastive loss based on Kullback-Leibler divergence. Extensive evaluations on MSR-VTT, MSVD, and VATEX benchmarks confirm that DAB significantly outperforms existing probabilistic and diffusion-based baselines, while providing calibrated uncertainty-aware ranking through bridge-induced distributional margins.