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
https://github.com/zhouyang-001/SMBG-for-temporal-action-proposal.

提出了一种新的模型 ——Sparse Multilevel Boundary Generator (SMBG)，通过边界分类和动作完整性回归来提高边界敏感方法的效率，引入了稀疏特征提取置信度头来优化提议特征生成器，并提出了全局指导损失来改善多支路之间的协同和平衡正负样本，在两个流行的基准 ActivityNet-1.3 和 THUMOS14 上评估，结果表明 SMBG 提供了一种更高效和简单的解决方案，可能会提高计算机视觉领域中视频分析中的时间动作定位的准确度和速度。