  Learning spatial-temporal relation among multiple actors is crucial for group
activity recognition. Different group activities often show the diversified
interactions between actors in the video. Hence, it is often difficult to model
complex group activities from a single view of spatial-temporal actor
evolution. To tackle this problem, we propose a distinct Dual-path Actor
Interaction (DualAI) framework, which flexibly arranges spatial and temporal
transformers in two complementary orders, enhancing actor relations by
integrating merits from different spatiotemporal paths. Moreover, we introduce
a novel Multi-scale Actor Contrastive Loss (MAC-Loss) between two interactive
paths of Dual-AI. Via self-supervised actor consistency in both frame and video
levels, MAC-Loss can effectively distinguish individual actor representations
to reduce action confusion among different actors. Consequently, our Dual-AI
can boost group activity recognition by fusing such discriminative features of
different actors. To evaluate the proposed approach, we conduct extensive
experiments on the widely used benchmarks, including Volleyball, Collective
Activity, and NBA datasets. The proposed Dual-AI achieves state-of-the-art
performance on all these datasets. It is worth noting the proposed Dual-AI with
50% training data outperforms a number of recent approaches with 100% training
data. This confirms the generalization power of Dual-AI for group activity
recognition, even under the challenging scenarios of limited supervision.
