无需训练，通过优化高斯边界实现视频片段精准定位
Finding Optimal Video Moment without Training: Gaussian Boundary Optimization for Weakly Supervised Video Grounding
- 提出高斯边界优化框架，从数学上求解最优时间边界
- 在多个基准上达到当前最佳性能，提升显著且无需额外训练
- 适用于多种提案结构，兼容性强，适合快速部署
弱监督视频定位旨在仅使用视频-句子对，在未剪辑视频中定位与查询相关的片段，无需精确的时间边界标注。现有方法利用基于高斯的时序提议来表示相关片段，但其推理依赖启发式规则从高斯参数映射边界，导致定位效果不佳。为此，本文提出高斯边界优化（GBO），一种新的推理框架，通过求解兼顾提议覆盖度与片段紧凑性的优化问题来预测边界。我们推导出该问题的闭式解，并严格分析了不同惩罚策略下的最优性条件。除了理论优势，GBO具有无需训练、兼容单高斯与混合高斯提案架构等实用特性。实验表明，GBO在多个标准基准上显著提升定位性能，达到当前最优结果。大量实验验证了其效率与泛化能力。代码已开源。
原文摘要 · Abstract (English)
Weakly supervised temporal video grounding aims to localize query-relevant segments in untrimmed videos using only video-sentence pairs, without requiring ground-truth segment annotations that specify exact temporal boundaries. Recent approaches tackle this task by utilizing Gaussian-based temporal proposals to represent query-relevant segments. However, their inference strategies rely on heuristic mappings from Gaussian parameters to segment boundaries, resulting in suboptimal localization performance. To address this issue, we propose Gaussian Boundary Optimization (GBO), a novel inference framework that predicts segment boundaries by solving a principled optimization problem that balances proposal coverage and segment compactness. We derive a closed-form solution for this problem and rigorously analyze the optimality conditions under varying penalty regimes. Beyond its theoretical foundations, GBO offers several practical advantages: it is training-free and compatible with both single-Gaussian and mixture-based proposal architectures. Our experiments show that GBO significantly improves localization, achieving state-of-the-art results across standard benchmarks. Extensive experiments demonstrate the efficiency and generalizability of GBO across various proposal schemes. The code is available at https://github.com/sunoh-kim/gbo.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。