This paper strives to recognize individual actions and group activities from
videos. While existing solutions for this challenging problem explicitly model
spatial and temporal relationships based on location of individual actors, we
propose an actor-transformer model able to learn and selectively extract
information relevant for group activity recognition. We feed the transformer
with rich actor-specific static and dynamic representations expressed by
features from a 2D pose network and 3D CNN, respectively. We empirically study
different ways to combine these representations and show their complementary
benefits. Experiments show what is important to transform and how it should be
transformed. What is more, actor-transformers achieve state-of-the-art results
on two publicly available benchmarks for group activity recognition,
outperforming the previous best published results by a considerable margin.

本文提出一种基于 actor-transformer model 认知视频中的个人行为和组群活动的模型，并通过 2D 姿态网络和 3D CNN 等方式提供丰富的个人动态和静态信息表示，从而在公开数据集上取得了显著的效果。

基于 Actor-Transformers 的群体活动识别

Actor-Transformers for Group Activity Recognition

We consider the problem of representing collective behavior of large
populations and predicting the evolution of a population distribution over a
discrete state space. A discrete time mean field game (MFG) is motivated as an
interpretable model founded on game theory for understanding the aggregate
effect of individual actions and predicting the temporal evolution of
population distributions. We achieve a synthesis of MFG and Markov decision
processes (MDP) by showing that a special MFG is reducible to an MDP. This
enables us to broaden the scope of mean field game theory and infer MFG models
of large real-world systems via deep inverse reinforcement learning. Our method
learns both the reward function and forward dynamics of an MFG from real data,
and we report the first empirical test of a mean field game model of a
real-world social media population.

本文介绍了一种结合了均场博弈和马尔科夫决策过程的模型，该模型能够解决大规模群体行为的表示和分布预测问题。通过深度逆强化学习，模型可以从真实数据中学习到均场博弈中的奖励函数和前向动态，以推断大型实际系统的均场博弈模型。首次将均场博弈模型应用到社交媒体人口研究领域。