Abstract
Despite recent advances, Text-to-video retrieval (TVR) is still hindered by multiple inherent uncertainties, such as ambiguous textual queries, indistinct text-video mappings, and low-quality video frames. Although interactive systems have emerged to address these challenges by refining user intent through clarifying questions, current methods typically rely on heuristic or ad-hoc strategies without explicitly quantifying these uncertainties, limiting their effectiveness. Motivated by this gap, we propose UMIVR, an Uncertainty-Minimizing Interactive Text-to-Video Retrieval framework that explicitly quantifies three critical uncertainties-text ambiguity, mapping uncertainty, and frame uncertainty-via principled, training-free metrics: semantic entropy-based Text Ambiguity Score (TAS), Jensen-Shannon divergence-based Mapping Uncertainty Score (MUS), and a Temporal Quality-based Frame Sampler (TQFS). By adaptively generating targeted clarifying questions guided by these uncertainty measures, UMIVR iteratively refines user queries, significantly reducing retrieval ambiguity. Extensive experiments on multiple benchmarks validate UMIVR's effectiveness, achieving notable gains in Recall@1 (69.2\% after 10 interactive rounds) on the MSR-VTT-1k dataset, thereby establishing an uncertainty-minimizing foundation for interactive TVR.
Abstract (translated)
尽管最近取得了进展，文本到视频检索（TVR）仍然受到多种内在不确定性的阻碍，例如模棱两可的文本查询、不明确的文本与视频映射关系以及低质量的视频帧。虽然交互式系统已经出现，通过提出澄清问题来细化用户意图以应对这些挑战，但当前的方法通常依赖于启发式或临时策略，并未显式量化这些不确定性，从而限制了其有效性。为了解决这一差距，我们提出了UMIVR（不确定性最小化的交互式文本到视频检索框架），该框架通过基于原则的、无训练的数据方法明确地量化三种关键不确定性：语义熵为基础的文本歧义评分（TAS）、基于杰森-香农散度的映射不确定评分（MUS）和基于时间质量的时间帧采样器（TQFS）。UMIVR能够根据这些不确定性衡量标准自适应生成有针对性的澄清问题，从而迭代细化用户查询，并显著减少检索模糊性。在多个基准测试中的广泛实验验证了UMIVR的有效性，在MSR-VTT-1k数据集上实现了Recall@1在10轮交互后达到69.2%的良好效果，为交互式TVR建立了基于最小化不确定性的基础。
URL
https://arxiv.org/abs/2507.15504
