PRVR: Partially Relevant Video Retrieval
2022 · Xianke Chen, Daizong Liu, Xun Yang, et al.
Abstract
In current text-to-video retrieval (T2VR), videos to be retrieved have been properly trimmed so that a correspondence between the videos and ad-hoc textual queries naturally exists. Note in practice that videos circulated on the Internet and social media platforms, while being relatively short, are typically rich in their content. Often, multiple scenes / actions / events are shown in a single video, leading to a more challenging T2VR setting wherein only part of the video content is relevant w.r.t. a given query. This paper presents a first study on this setting which we term Partially Relevant Video Retrieval (PRVR). Considering that a video typically consists of multiple moments, a video is regarded as partially relevant w.r.t. to a given query if it contains a query-related moment. We formulate the PRVR task as a multiple instance learning problem, and propose a Multi-Scale Similarity Learning (MS-SL++) network that jointly learns both clip-scale and frame-scale similarities to det
Authors
(none)
Tags
Stats
Related papers
- Ambiguity-restrained Text-video Representation Learning For Partially Relevant Video Retrieval (2025)5.84
- Uneven Event Modeling For Partially Relevant Video Retrieval (2025)1.40
- Mitigating Semantic Collapse In Partially Relevant Video Retrieval (2025)0.00
- Gmmformer: Gaussian-mixture-model Based Transformer For Efficient Partially Relevant Video Retrieval (2023)12.06
- Imagine Before Concentration: Diffusion-guided Registers Enhance Partially Relevant Video Retrieval (2026)3.80
- Mamfusion: Multi-mamba With Temporal Fusion For Partially Relevant Video Retrieval (2025)1.69
- Dual Learning With Dynamic Knowledge Distillation And Soft Alignment For Partially Relevant Video Retrieval (2025)2.60
- Prototypes Are Balanced Units For Efficient And Effective Partially Relevant Video Retrieval (2025)0.00