Problem
Research questions and friction points this paper is trying to address.
text-to-video retrieval
uncertainty modeling
distribution alignment
cross-modal retrieval
probabilistic embedding
Distribution-Alignment Bridge for Uncertainty-Aware Text-to-Video Retrieval
🤖 AI Summary
This work addresses the limitations of existing text-to-video retrieval methods, which typically rely on deterministic point matching and overlook modality-specific uncertainties, thereby struggling to accurately capture cross-modal semantic alignment. The paper reframes the task as a distribution alignment problem and introduces Gaussian embeddings to model uncertainty in both textual and visual representations. Inspired by diffusion processes, it proposes a deterministic distribution bridging mechanism that integrates truncated iterative optimization with a KL divergence–based distribution-aware contrastive loss, enabling end-to-end training. The approach substantially outperforms current probabilistic and diffusion-based baselines on MSR-VTT, MSVD, and VATEX benchmarks, while producing well-calibrated, uncertainty-aware retrieval rankings.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
distribution alignment
uncertainty-aware retrieval
probabilistic embedding
diffusion-inspired bridge
Kullback-Leibler contrastive loss
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