This paper proposes Group Activity Feature (GAF) learning in which features
of multi-person activity are learned as a compact latent vector. Unlike prior
work in which the manual annotation of group activities is required for
supervised learning, our method learns the GAF through person attribute
prediction without group activity annotations. By learning the whole network in
an end-to-end manner so that the GAF is required for predicting the person
attributes of people in a group, the GAF is trained as the features of
multi-person activity. As a person attribute, we propose to use a person's
action class and appearance features because the former is easy to annotate due
to its simpleness, and the latter requires no manual annotation. In addition,
we introduce a location-guided attribute prediction to disentangle the complex
GAF for extracting the features of each target person properly. Various
experimental results validate that our method outperforms SOTA methods
quantitatively and qualitatively on two public datasets. Visualization of our
GAF also demonstrates that our method learns the GAF representing fined-grained
group activity classes. Code: this https URL

该研究提出了组活动特征（GAF）学习，通过人物属性预测无需群体活动注释来学习多人活动的特征；结合端到端训练方式，将 GAF 用于预测群体中人物属性，从而作为多人活动的特征；通过引入基于位置的属性预测，能够正确提取每个目标人物的复杂 GAF 特征；实验结果表明该方法在两个公共数据集上在定量和定性上都优于最先进方法，同时 GAF 的可视化证明了该方法学习到了细粒度的群体活动类特征。