Sign language video retrieval plays a key role in facilitating information access for the deaf community. Despite significant advances in video-text retrieval, the complexity and inherent uncertainty of sign language preclude the direct application of these techniques. Previous methods achieve the mapping between sign language video and text through fine-grained modal alignment. However, due to the scarcity of fine-grained annotation, the uncertainty inherent in sign language video is underestimated, limiting the further development of sign language retrieval tasks. To address this challenge, we propose a novel Uncertainty-aware Probability Distribution Retrieval (UPRet), that conceptualizes the mapping process of sign language video and text in terms of probability distributions, explores their potential interrelationships, and enables flexible mappings. Experiments on three benchmarks demonstrate the effectiveness of our method, which achieves state-of-the-art results on How2Sign (59.1%), PHOENIX-2014T (72.0%), and CSL-Daily (78.4%).

提出了一种新的基于不确定性感知的概率分布检索 (UPRet) 方法，以概率分布的形式理解手语视频和文本之间的映射过程，并探索它们的潜在相互关系，实现了灵活的映射。在三个基准测试上进行的实验展示了我们方法的有效性，分别在 How2Sign (59.1%)、PHOENIX-2014T (72.0%) 和 CSL-Daily (78.4%) 上取得了最先进的结果。

基于概率分布建模的不确定性感知手语视频检索