  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%).
