Social group activity recognition is a challenging task extended from group
activity recognition, where social groups must be recognized with their
activities and group members. Existing methods tackle this task by leveraging
region features of individuals following existing group activity recognition
methods. However, the effectiveness of region features is susceptible to person
localization and variable semantics of individual actions. To overcome these
issues, we propose leveraging attention modules in transformers to generate
social group features. In this method, multiple embeddings are used to
aggregate features for a social group, each of which is assigned to a group
member without duplication. Due to this non-duplicated assignment, the number
of embeddings must be significant to avoid missing group members and thus
renders attention in transformers ineffective. To find optimal attention
designs with a large number of embeddings, we explore several design choices of
queries for feature aggregation and self-attention modules in transformer
decoders. Extensive experimental results show that the proposed method achieves
state-of-the-art performance and verify that the proposed attention designs are
highly effective on social group activity recognition.

社会群体活动识别研究了使用注意力模块和变压器来生成社会群体特征，并通过优化设计特征聚合和自注意力模块的方法来提高活动识别效果。

社会团体活动识别中高效注意力变换器的设计与分析

Design and Analysis of Efficient Attention in Transformers for Social  Group Activity Recognition

This paper introduces a novel approach to Social Group Activity Recognition
(SoGAR) using Self-supervised Transformers network that can effectively utilize
unlabeled video data. To extract spatio-temporal information, we create local
and global views with varying frame rates. Our self-supervised objective
ensures that features extracted from contrasting views of the same video are
consistent across spatio-temporal domains. Our proposed approach is efficient
in using transformer-based encoders for alleviating the weakly supervised
setting of group activity recognition. By leveraging the benefits of
transformer models, our approach can model long-term relationships along
spatio-temporal dimensions. Our proposed SoGAR method achieves state-of-the-art
results on three group activity recognition benchmarks, namely JRDB-PAR, NBA,
and Volleyball datasets, surpassing the current state-of-the-art in terms of
F1-score, MCA, and MPCA metrics.

本文提出了一种使用自我监督变压器网络的社交组活动识别（SoGAR）的新方法，可以有效利用未标记的视频数据，通过利用变化的帧率创建局部和全局视图来提取空时信息，并通过提取来自同一视频的对比视图的特征的自我监督目标，确保跨空时域中的特征是一致的。该方法在使用变压器模型方面具有高效性，可以沿着空时维度建模长期关系，并在三个小组活动识别基准测试中取得了最新的成果，在 F1-score，MCA 和 MPCA 指标方面超越了当前最新技术。

自监督的时空关注社交群体活动识别 (SoGAR)

SoGAR: Self-supervised Spatiotemporal Attention-based Social Group  Activity Recognition

This paper presents a novel framework for social group activity recognition.
As an expanded task of group activity recognition, social group activity
recognition requires recognizing multiple sub-group activities and identifying
group members. Most existing methods tackle both tasks by refining region
features and then summarizing them into activity features. Such heuristic
feature design renders the effectiveness of features susceptible to incomplete
person localization and disregards the importance of scene contexts.
Furthermore, region features are sub-optimal to identify group members because
the features may be dominated by those of people in the regions and have
different semantics. To overcome these drawbacks, we propose to leverage
attention modules in transformers to generate effective social group features.
Our method is designed in such a way that the attention modules identify and
then aggregate features relevant to social group activities, generating an
effective feature for each social group. Group member information is embedded
into the features and thus accessed by feed-forward networks. The outputs of
feed-forward networks represent groups so concisely that group members can be
identified with simple Hungarian matching between groups and individuals.
Experimental results show that our method outperforms state-of-the-art methods
on the Volleyball and Collective Activity datasets.

本论文提出了一种基于注意力模块和 transformers 模型的社交群组活动识别新框架，该方法在 Volleyball 和 Collective Activity 数据集上表现优于现有技术。