用简单结构实现视频定位新基准，效果媲美复杂模型。
SimBase: A Simple Baseline for Temporal Video Grounding
- 用一维卷积替代复杂时序结构，简化时序建模
- 仅用逐元素乘积实现跨模态交互，性能达顶尖水平
- 适合想快速验证新想法的研究者参考
本文提出 SimBase，一个用于时序视频定位的简单但高效的基线方法。尽管近期研究在该任务上取得了显著进展，但网络架构趋向复杂，普遍依赖多种机制来捕捉时序关系和实现有效的多模态融合。与此相反，本文探讨：简化方法能达到怎样的效果？为此，设计了 SimBase，采用轻量级的一维时间卷积层替代复杂的时序结构，并仅使用逐元素乘积进行跨模态交互，避免繁琐的融合设计。令人惊讶的是，SimBase 在两个大规模数据集上均达到当前最优性能。作为一个简洁而强大的基线，我们希望 SimBase 能激发新思路，并推动未来时序视频定位研究的评估标准化。
原文摘要 · Abstract (English)
This paper presents SimBase, a simple yet effective baseline for temporal video grounding. While recent advances in temporal grounding have led to impressive performance, they have also driven network architectures toward greater complexity, with a range of methods to (1) capture temporal relationships and (2) achieve effective multimodal fusion. In contrast, this paper explores the question: How effective can a simplified approach be? To investigate, we design SimBase, a network that leverages lightweight, one-dimensional temporal convolutional layers instead of complex temporal structures. For cross-modal interaction, SimBase only employs an element-wise product instead of intricate multimodal fusion. Remarkably, SimBase achieves state-of-the-art results on two large-scale datasets. As a simple yet powerful baseline, we hope SimBase will spark new ideas and streamline future evaluations in temporal video grounding.
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