Temporal action proposal generation plays an important role in video action
understanding, which requires localizing high-quality action content precisely.
However, generating temporal proposals with both precise boundaries and
high-quality action content is extremely challenging. To address this issue, we
propose a novel Boundary Content Graph Neural Network (BC-GNN) to model the
insightful relations between the boundary and action content of temporal
proposals by the graph neural networks. In BC-GNN, the boundaries and content
of temporal proposals are taken as the nodes and edges of the graph neural
network, respectively, where they are spontaneously linked. Then a novel graph
computation operation is proposed to update features of edges and nodes. After
that, one updated edge and two nodes it connects are used to predict boundary
probabilities and content confidence score, which will be combined to generate
a final high-quality proposal. Experiments are conducted on two mainstream
datasets: ActivityNet-1.3 and THUMOS14. Without the bells and whistles, BC-GNN
outperforms previous state-of-the-art methods in both temporal action proposal
and temporal action detection tasks.

提出了一种边界内容图神经网络（BC-GNN）来生成高质量的时间性建议，通过神经网络模拟时间性建议的边界和行动内容之间的相互关系，显著提高了行动理解的准确性和效率。

边界内容图神经网络用于时序动作提案生成

Boundary Content Graph Neural Network for Temporal Action Proposal  Generation

In this notebook paper, we describe our approach in the submission to the
temporal action proposal (task 3) and temporal action localization (task 4) of
ActivityNet Challenge hosted at CVPR 2017. Since the accuracy in action
classification task is already very high (nearly 90% in ActivityNet dataset),
we believe that the main bottleneck for temporal action localization is the
quality of action proposals. Therefore, we mainly focus on the temporal action
proposal task and propose a new proposal model based on temporal convolutional
network. Our approach achieves the state-of-the-art performances on both
temporal action proposal task and temporal action localization task.

本研究旨在提出一种基于时间卷积网络的新型提议模型以解决时间行为定位中行为提议的低质量问题，并在该领域取得了最先进的性能结果。

基于时间卷积的动作提议：ActivityNet 2017 提交

Temporal Convolution Based Action Proposal: Submission to ActivityNet  2017

Detecting actions in untrimmed videos is an important yet challenging task.
In this paper, we present the structured segment network (SSN), a novel
framework which models the temporal structure of each action instance via a
structured temporal pyramid. On top of the pyramid, we further introduce a
decomposed discriminative model comprising two classifiers, respectively for
classifying actions and determining completeness. This allows the framework to
effectively distinguish positive proposals from background or incomplete ones,
thus leading to both accurate recognition and localization. These components
are integrated into a unified network that can be efficiently trained in an
end-to-end fashion. Additionally, a simple yet effective temporal action
proposal scheme, dubbed temporal actionness grouping (TAG) is devised to
generate high quality action proposals. On two challenging benchmarks, THUMOS14
and ActivityNet, our method remarkably outperforms previous state-of-the-art
methods, demonstrating superior accuracy and strong adaptivity in handling
actions with various temporal structures.

本文提出了一种基于结构化分割网络 (SSN) 的新型框架，该框架可以通过一个结构化的时间金字塔模型来建模每个动作实例的时间结构，并通过划分的判别模型有效地区分正面提议和背景或不完整的提议，在 THUMOS14 和 ActivityNet 数据集上表现优于现有方法。

结构分段网络在时间动作检测中的应用

Temporal Action Detection with Structured Segment Networks

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。