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arXiv 2303.03166cs.CVcs.AI
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基于稀疏多层边界生成器的时态动作建议快速学习

Faster Learning of Temporal Action Proposal via Sparse Multilevel Boundary Generator

Beijing University of Posts and Telecommunications（北京邮电大学）

Qing Song, Yang Zhou, Mengjie Hu, Chun Liu

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18 pages, 5 figures
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摘要：

视频中的时间动作定位在计算机视觉领域提出了重大挑战。虽然边界敏感方法已被广泛采用，但其局限性包括中间和全局信息的不完整使用，以及效率低下的建议特征生成器。为了解决这些挑战，我们提出了一种新的框架，稀疏多级边界生成器（SMBG），它增强了边界敏感的方法与边界分类和动作完整性回归。SMBG具有多级边界模块，通过收集不同长度的边界信息来加快处理速度。此外，我们引入了一个稀疏提取置信头，区分信息内部和外部的行动，进一步优化的建议功能生成器。为了提高多个分支之间的协同性和平衡正负样本，我们提出了一个全局指导损失。我们的方法在两个流行的基准测试ActivityNet-1.3和THUMOS 14上进行了评估，并显示出达到最先进的性能，具有更好的推理速度（2.47xBSN++，2.12xDBG）。这些结果表明，SMBG提供了一个更有效和简单的解决方案，产生时间的行动建议。我们提出的框架有可能推进计算机视觉领域，提高视频分析中时间动作定位的准确性和速度。代码和模型可在\url{https://github.com/zhouyang-001/SMBG-for-temporal-action-proposal}上获得。

英文摘要：

Temporal action localization in videos presents significant challenges in the field of computer vision. While the boundary-sensitive method has been widely adopted, its limitations include incomplete use of intermediate and global information, as well as an inefficient proposal feature generator. To address these challenges, we propose a novel framework, Sparse Multilevel Boundary Generator (SMBG), which enhances the boundary-sensitive method with boundary classification and action completeness regression. SMBG features a multi-level boundary module that enables faster processing by gathering boundary information at different lengths. Additionally, we introduce a sparse extraction confidence head that distinguishes information inside and outside the action, further optimizing the proposal feature generator. To improve the synergy between multiple branches and balance positive and negative samples, we propose a global guidance loss. Our method is evaluated on two popular benchmarks, ActivityNet-1.3 and THUMOS14, and is shown to achieve state-of-the-art performance, with a better inference speed (2.47xBSN++, 2.12xDBG). These results demonstrate that SMBG provides a more efficient and simple solution for generating temporal action proposals. Our proposed framework has the potential to advance the field of computer vision and enhance the accuracy and speed of temporal action localization in video analysis.The code and models are made available at \url{https://github.com/zhouyang-001/SMBG-for-temporal-action-proposal}.

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。

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