Temporal Action Proposal (TAP) generation is an important problem, as fast
and accurate extraction of semantically important (e.g. human actions) segments
from untrimmed videos is an important step for large-scale video analysis. We
propose a novel Temporal Unit Regression Network (TURN) model. There are two
salient aspects of TURN: (1) TURN jointly predicts action proposals and refines
the temporal boundaries by temporal coordinate regression; (2) Fast computation
is enabled by unit feature reuse: a long untrimmed video is decomposed into
video units, which are reused as basic building blocks of temporal proposals.
TURN outperforms the state-of-the-art methods under average recall (AR) by a
large margin on THUMOS-14 and ActivityNet datasets, and runs at over 880 frames
per second (FPS) on a TITAN X GPU. We further apply TURN as a proposal
generation stage for existing temporal action localization pipelines, it
outperforms state-of-the-art performance on THUMOS-14 and ActivityNet.

提出了一种基于 Temporal Unit Regression Network 的 Temporal Action Proposal generation 方法，通过时间坐标回归预测动作提议并改进时间边界，利用视频单元作为时间提议的基本构建块，优化了模型的计算速度，该模型在 THUMOS-14 和 ActivityNet 数据集上表现出较高的性能。