OVG-HQ: Online Video Grounding with Hybrid-modal Queries

TL;DR

This paper introduces OVG-HQ, a new online video grounding task with hybrid-modal queries, proposing a unified model and dataset to handle online, multi-modal video localization challenges effectively.

Contribution

We propose OVG-HQ-Unify, a novel framework with a Parametric Memory Block and cross-modal distillation to improve hybrid-modal online video grounding.

Findings

Our model outperforms existing methods in accuracy and efficiency.

The new dataset QVHighlights-Unify enables comprehensive evaluation.

Adapted online metrics effectively measure real-time performance.

Abstract

Video grounding (VG) task focuses on locating specific moments in a video based on a query, usually in text form. However, traditional VG struggles with some scenarios like streaming video or queries using visual cues. To fill this gap, we present a new task named Online Video Grounding with Hybrid-modal Queries (OVG-HQ), which enables online segment localization using text, images, video segments, and their combinations. This task poses two new challenges: limited context in online settings and modality imbalance during training, where dominant modalities overshadow weaker ones. To address these, we propose OVG-HQ-Unify, a unified framework featuring a Parametric Memory Block (PMB) that retain previously learned knowledge to enhance current decision and a cross-modal distillation strategy that guides the learning of non-dominant modalities. This design enables a single model to effectively handle hybrid-modal queries. Due to the lack of suitable datasets, we construct QVHighlights-Unify, an expanded dataset with multi-modal queries. Besides, since offline metrics overlook prediction timeliness, we adapt them to the online setting, introducing oR@n, IoU=m, and online mean Average Precision (omAP) to evaluate both accuracy and efficiency. Experiments show that our OVG-HQ-Unify outperforms existing models, offering a robust solution for online, hybrid-modal video grounding. Source code and datasets are available at https://github.com/maojiaqi2324/OVG-HQ.