  Given a text query, partially relevant video retrieval (PRVR) aims to
retrieve untrimmed videos containing relevant moments. Due to the lack of
moment annotations, the uncertainty lying in clip modeling and text-clip
correspondence leads to major challenges. Despite the great progress, existing
solutions either sacrifice efficiency or efficacy to capture varying and
uncertain video moments. What's worse, few methods have paid attention to the
text-clip matching pattern under such uncertainty, exposing the risk of
semantic collapse. To address these issues, we present GMMFormer v2, an
uncertainty-aware framework for PRVR. For clip modeling, we improve a strong
baseline GMMFormer with a novel temporal consolidation module upon multi-scale
contextual features, which maintains efficiency and improves the perception for
varying moments. To achieve uncertainty-aware text-clip matching, we upgrade
the query diverse loss in GMMFormer to facilitate fine-grained uniformity and
propose a novel optimal matching loss for fine-grained text-clip alignment.
Their collaboration alleviates the semantic collapse phenomenon and neatly
promotes accurate correspondence between texts and moments. We conduct
extensive experiments and ablation studies on three PRVR benchmarks,
demonstrating remarkable improvement of GMMFormer v2 compared to the past SOTA
competitor and the versatility of uncertainty-aware text-clip matching for
PRVR. Code is available at
\url{https://github.com/huangmozhi9527/GMMFormer_v2}.
