Accurate temporal action proposals play an important role in detecting
actions from untrimmed videos. The existing approaches have difficulties in
capturing global contextual information and simultaneously localizing actions
with different durations. To this end, we propose a Relation-aware pyramid
Network (RapNet) to generate highly accurate temporal action proposals. In
RapNet, a novel relation-aware module is introduced to exploit bi-directional
long-range relations between local features for context distilling. This
embedded module enhances the RapNet in terms of its multi-granularity temporal
proposal generation ability, given predefined anchor boxes. We further
introduce a two-stage adjustment scheme to refine the proposal boundaries and
measure their confidence in containing an action with snippet-level actionness.
Extensive experiments on the challenging ActivityNet and THUMOS14 benchmarks
demonstrate our RapNet generates superior accurate proposals over the existing
state-of-the-art methods.

介绍了 Relation-aware pyramid Network (RapNet) 方法，通过引入新的关系感知模块，同时利用局部特征之间的双向长程关系进行上下文提炼，提升其多层次时间建议生成能力，进而产生具有时间和置信度较高的动作建议。该方法在 ActivityNet 和 THUMOS14 数据集上的实验结果表明优于现有的最先进方法。