  Most existing group activity recognition methods construct spatial-temporal
relations merely based on visual representation. Some methods introduce extra
knowledge, such as action labels, to build semantic relations and use them to
refine the visual presentation. However, the knowledge they explored just stay
at the semantic-level, which is insufficient for pursing notable accuracy. In
this paper, we propose to exploit knowledge concretization for the group
activity recognition, and develop a novel Knowledge Augmented Relation
Inference framework that can effectively use the concretized knowledge to
improve the individual representations. Specifically, the framework consists of
a Visual Representation Module to extract individual appearance features, a
Knowledge Augmented Semantic Relation Module explore semantic representations
of individual actions, and a Knowledge-Semantic-Visual Interaction Module aims
to integrate visual and semantic information by the knowledge. Benefiting from
these modules, the proposed framework can utilize knowledge to enhance the
relation inference process and the individual representations, thus improving
the performance of group activity recognition. Experimental results on two
public datasets show that the proposed framework achieves competitive
performance compared with state-of-the-art methods.
