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Learning spatial-temporal relation among multiple actors is crucial for groupactivity recognition. Different group activities often show the diversifiedinteractions between actors in the video. Hence, it is often difficult to modelcomplex group activities from a single view of spatial-temporal actorevolution. To tackle this problem, we propose a distinct Dual-path ActorInteraction (DualAI) framework, which flexibly arranges spatial and temporaltransformers in two complementary orders, enhancing actor relations byintegrating merits from different spatiotemporal paths. Moreover, we introducea novel Multi-scale Actor Contrastive Loss (MAC-Loss) between two interactivepaths of Dual-AI. Via self-supervised actor consistency in both frame and videolevels, MAC-Loss can effectively distinguish individual actor representationsto reduce action confusion among different actors. Consequently, our Dual-AIcan boost group activity recognition by fusing such discriminative features ofdifferent actors. To evaluate the proposed approach, we conduct extensiveexperiments on the widely used benchmarks, including Volleyball, CollectiveActivity, and NBA datasets. The proposed Dual-AI achieves state-of-the-artperformance on all these datasets. It is worth noting the proposed Dual-AI with50% training data outperforms a number of recent approaches with 100% trainingdata. This confirms the generalization power of Dual-AI for group activityrecognition, even under the challenging scenarios of limited supervision.

Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition