Deep neural networks based purely on attention have been successful across
several domains, relying on minimal architectural priors from the designer. In
Human Action Recognition (HAR), attention mechanisms have been primarily
adopted on top of standard convolutional or recurrent layers, improving the
overall generalization capability. In this work, we introduce Action
Transformer (AcT), a simple, fully self-attentional architecture that
consistently outperforms more elaborated networks that mix convolutional,
recurrent and attentive layers. In order to limit computational and energy
requests, building on previous human action recognition research, the proposed
approach exploits 2D pose representations over small temporal windows,
providing a low latency solution for accurate and effective real-time
performance. Moreover, we open-source MPOSE2021, a new large-scale dataset, as
an attempt to build a formal training and evaluation benchmark for real-time,
short-time HAR. The proposed methodology was extensively tested on MPOSE2021
and compared to several state-of-the-art architectures, proving the
effectiveness of the AcT model and laying the foundations for future work on
HAR.

本文介绍了 Action Transformer，一种简单的完全自注意力架构，在人体动作识别中表现出色，并利用 2D 姿势表示来提供低延迟解决方案，此外我们提供了 MPOSE2021，这是一个新的大规模数据集，用于实时、短时 HAR 的训练和评估基准建立。

Action Transformer：用于基于短时间姿态的人类动作识别的自注意模型

Action Transformer: A Self-Attention Model for Short-Time Pose-Based  Human Action Recognition

We introduce the Action Transformer model for recognizing and localizing
human actions in video clips. We repurpose a Transformer-style architecture to
aggregate features from the spatiotemporal context around the person whose
actions we are trying to classify. We show that by using high-resolution,
person-specific, class-agnostic queries, the model spontaneously learns to
track individual people and to pick up on semantic context from the actions of
others. Additionally its attention mechanism learns to emphasize hands and
faces, which are often crucial to discriminate an action - all without explicit
supervision other than boxes and class labels. We train and test our Action
Transformer network on the Atomic Visual Actions (AVA) dataset, outperforming
the state-of-the-art by a significant margin using only raw RGB frames as
input.

本文介绍了行动转换器模型用于识别和定位视频剪辑中的人类动作，使用 Transformer 风格的架构聚合人物周围的时空背景特征，通过高分辨率、个性化、类别不可知的查询，该模型自动学习跟踪个人并从他人的动作中获取语义上下文