Distribution-Alignment Bridge for Uncertainty-Aware Text-to-Video Retrieval

• URL: http://arxiv.org/abs/2607.20984v1
• Date: Thu, 23 Jul 2026 07:07:28 GMT
• Title: Distribution-Alignment Bridge for Uncertainty-Aware Text-to-Video Retrieval
• Abstract summary: This paper proposes a framework that reconceptualizes text-to-video retrieval as a distribution alignment task rather than traditional deterministic point matching.<n>By modeling both text and video embeddings as Gaussian distributions defined by mean and variance, DAB explicitly accounts for modality-specific uncertainty.<n>We employ a deterministic, diffusion-inspired bridge to iteratively refine text distributions toward their target video distributions through a truncated refinement process.
• Score: 6.5819669635975915
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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