Generating temporal action proposals remains a very challenging problem,
where the main issue lies in predicting precise temporal proposal boundaries
and reliable action confidence in long and untrimmed real-world videos. In this
paper, we propose an efficient and unified framework to generate temporal
action proposals named Dense Boundary Generator (DBG), which draws inspiration
from boundary-sensitive methods and implements boundary classification and
action completeness regression for densely distributed proposals. In
particular, the DBG consists of two modules: Temporal boundary classification
(TBC) and Action-aware completeness regression (ACR). The TBC aims to provide
two temporal boundary confidence maps by low-level two-stream features, while
the ACR is designed to generate an action completeness score map by high-level
action-aware features. Moreover, we introduce a dual stream BaseNet (DSB) to
encode RGB and optical flow information, which helps to capture discriminative
boundary and actionness features. Extensive experiments on popular benchmarks
ActivityNet-1.3 and THUMOS14 demonstrate the superiority of DBG over the
state-of-the-art proposal generator (e.g., MGG and BMN). Our code will be made
available upon publication.

本篇论文提出了一种名为 Dense Boundary Generator (DBG) 的生成时间性行动提案的高效统一框架，该框架通过实现边界分类和行动完整性回归以获取密集分布提案中的精确时间提案边界和可靠的行动置信度