Spatio-Temporal Dynamic Inference Network for Group Activity Recognition

ICCV 2021  ·  Hangjie Yuan, Dong Ni, Mang Wang ·

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.

PDF Abstract ICCV 2021 PDF ICCV 2021 Abstract


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Group Activity Recognition Volleyball DIN (VGG16) Accuracy 93.6 # 3


No methods listed for this paper. Add relevant methods here