Abstract
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 \href{this https URL}{this https URL}.
Abstract (translated)
弱监督视频时间定位的目标是在未裁剪的视频中仅使用视频-句子对来定位与查询相关的片段，而不需依赖明确指定具体时间边界的地面真实标注。最近的研究方法通过利用基于高斯分布的时间提案来表示与查询相关的片段。然而，这些方法的推理策略依赖于从高斯参数到段落边界之间的启发式映射，这导致了次优的时间定位性能。为了解决这个问题，我们提出了高斯边界优化（GBO），这是一种新的推断框架，它通过解决一个平衡提案覆盖范围和片段紧实度的原则性优化问题来预测片段边界。我们为该问题导出了闭合形式的解决方案，并严格分析了在不同惩罚制度下的最优条件。 除了其理论基础外，GBO还提供了许多实用优势：它是无须训练且与单个高斯和混合型提案架构兼容的。我们的实验表明，GBO显著提高了定位性能，在标准基准测试中取得了最先进的结果。广泛的实验证明了GBO在各种提案方案中的效率和泛化能力。代码可在[此处](this https URL)获取。 原文的意思是介绍了一种新的弱监督视频时间定位方法——高斯边界优化（Gaussian Boundary Optimization, GBO），这种方法通过解决一个平衡提案覆盖范围与片段紧实度的原则性优化问题，提高了定位的准确性，并且无须额外训练。这项技术在标准基准测试中取得了领先的成绩，并展示了其在多种提案方案中的高效性和泛化能力。
URL
https://arxiv.org/abs/2602.03071
