  Group activity recognition aims to understand the activity performed by a
group of people. In order to solve it, modeling complex spatio-temporal
interactions is the key. Previous methods are limited in reasoning on a
predefined graph, which ignores the inherent person-specific interaction
context. Moreover, they adopt inference schemes that are computationally
expensive and easily result in the over-smoothing problem. In this paper, we
manage to achieve spatio-temporal person-specific inferences by proposing
Dynamic Inference Network (DIN), which composes of Dynamic Relation (DR) module
and Dynamic Walk (DW) module. We firstly propose to initialize interaction
fields on a primary spatio-temporal graph. Within each interaction field, we
apply DR to predict the relation matrix and DW to predict the dynamic walk
offsets in a joint-processing manner, thus forming a person-specific
interaction graph. By updating features on the specific graph, a person can
possess a global-level interaction field with a local initialization.
Experiments indicate both modules' effectiveness. Moreover, DIN achieves
significant improvement compared to previous state-of-the-art methods on two
popular datasets under the same setting, while costing much less computation
overhead of the reasoning module.
