In recent years, assessing action quality from videos has attracted growing
attention in computer vision community and human computer interaction. Most
existing approaches usually tackle this problem by directly migrating the model
from action recognition tasks, which ignores the intrinsic differences within
the feature map such as foreground and background information. To address this
issue, we propose a Tube Self-Attention Network (TSA-Net) for action quality
assessment (AQA). Specifically, we introduce a single object tracker into AQA
and propose the Tube Self-Attention Module (TSA), which can efficiently
generate rich spatio-temporal contextual information by adopting sparse feature
interactions. The TSA module is embedded in existing video networks to form
TSA-Net. Overall, our TSA-Net is with the following merits: 1) High
computational efficiency, 2) High flexibility, and 3) The state-of-the art
performance. Extensive experiments are conducted on popular action quality
assessment datasets including AQA-7 and MTL-AQA. Besides, a dataset named Fall
Recognition in Figure Skating (FR-FS) is proposed to explore the basic action
assessment in the figure skating scene.

本文提出了一种基于 Tube Self-Attention Network 的方法来评估视频中行动的质量，并通过引入单个对象跟踪器和 Tube Self-Attention Module 来提取丰富的时空上下文信息。此外，在 AQA-7, MTL-AQA 数据集上进行广泛实验，并提出了一个名为 FR-FS 的数据集以探究花样滑冰场景中的基本行动评估。

TSA-Net：管道自注意力网络用于动作质量评估

TSA-Net: Tube Self-Attention Network for Action Quality Assessment

This paper presents X3D, a family of efficient video networks that
progressively expand a tiny 2D image classification architecture along multiple
network axes, in space, time, width and depth. Inspired by feature selection
methods in machine learning, a simple stepwise network expansion approach is
employed that expands a single axis in each step, such that good accuracy to
complexity trade-off is achieved. To expand X3D to a specific target
complexity, we perform progressive forward expansion followed by backward
contraction. X3D achieves state-of-the-art performance while requiring 4.8x and
5.5x fewer multiply-adds and parameters for similar accuracy as previous work.
Our most surprising finding is that networks with high spatiotemporal
resolution can perform well, while being extremely light in terms of network
width and parameters. We report competitive accuracy at unprecedented
efficiency on video classification and detection benchmarks. Code will be
available at: this https URL

本文提出了 X3D，一个高效的视频网络家族，可沿多个网络轴（在空间、时间、宽度和深度方面）逐步扩展微小的 2D 图像分类架构。采用类似于机器学习中的特征选择方法的简单逐步网络扩展方法，在每个步骤中扩展单个轴，从而实现对于复杂性的良好准确性权衡。通过向前递推扩展和向后收缩，将 X3D 扩展到特定目标复杂性。与以前的工作相比，X3D 实现了最先进的性能，同时需要较少的乘加和参数。我们最令人惊讶的发现是，具有高空间时间分辨率的网络可以表现出色，同时在网络宽度和参数方面非常轻量化。我们在视频分类和检测基准测试上报告了具有竞争力的准确性，代码将可在此 https URL 上获得。