无需训练即可找到最优视频片段：用于弱监督视频定位的高斯边界优化
Finding Optimal Video Moment without Training: Gaussian Boundary Optimization for Weakly Supervised Video Grounding
AI总结：
本文提出GBO方法，通过解决优化问题提升弱监督视频定位的定位性能，无需训练且兼容多种提案架构。
摘要：
弱监督时间视频定位旨在利用仅有的视频-句子对来定位查询相关的视频片段，而无需要求具有精确时间边界标注的地面真实片段。最近的方法通过利用基于高斯的时序提案来表示查询相关的片段。然而，它们的推理策略依赖于从高斯参数到片段边界的启发式映射，导致定位性能不理想。为了解决这个问题，我们提出了高斯边界优化（GBO），一种新的推理框架，通过解决一个平衡提案覆盖和片段紧凑性的原则性优化问题来预测片段边界。我们为该问题推导出闭合形式的解，并在不同的惩罚制度下严格分析最优条件。除了其理论基础外，GBO还提供了几个实际优势：它无需训练，并且与单高斯和混合提案架构兼容。我们的实验表明，GBO显著提高了定位性能，在标准基准上取得了最先进的结果。广泛的实验展示了GBO在各种提案方案中的效率和通用性。代码可在https://github.com/sunoh-kim/gbo上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
