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}.

给定一个文本查询，部分相关视频检索（PRVR）旨在检索包含相关片段的未修剪视频。为了解决缺乏片段注释以及剪辑建模和文本-剪辑对应所带来的不确定性，我们提出了一个针对PRVR的具有不确定性感知能力的GMMFormer v2框架。通过改进GMMFormer的剪辑建模和文本-剪辑匹配，我们有效地解决了语义崩塌现象，并提高了文本和片段之间的准确对应。我们在三个PRVR基准上进行了大量实验和消融研究，展示了GMMFormer v2相对于过去的最佳方法竞争者的显着改进，以及不确定性感知文本-剪辑匹配在PRVR中的多样性。

GMMFormer v2: 一个对部分相关视频检索问题具有不确定性意识的框架