Synchronised and Fine-Grained Head for Skeleton-Based Ambiguous Action Recognition

Abstract

Skeleton-based action recognition using Graph Convolutional Networks (GCNs) has achieved remarkable performance, but recognising ambiguous actions, such as ‘waving’ and ‘saluting’, remains a significant challenge. Existing methods typically rely on a serial combination of GCNs and Temporal Convolutional Networks (TCNs), where spatial and temporal features are extracted independently, leading to an unbalanced spatial-temporal information, which hinders accurate action recognition. Moreover, existing methods for ambiguous actions often overemphasise local details, resulting in the loss of crucial global context, which further complicates the task of differentiating ambiguous actions. To address these challenges, the authors propose a lightweight plug-and-play module called Synchronised and Fine-grained Head (SF-Head), inserted between GCN and TCN layers. SF-Head first conducts Synchronised Spatial-Temporal Extraction (SSTE) with a Feature Redundancy Loss (F-RL), ensuring a balanced interaction between the two types of features. It then performs Adaptive Cross-dimensional Feature Aggregation (AC-FA), with a Feature Consistency Loss (F-CL), which aligns the aggregated feature with their original spatial-temporal feature. This aggregation step effectively combines both global context and local details, enhancing the model's ability to classify ambiguous actions. Experimental results on NTU RGB + D 60, NTU RGB + D 120, NW-UCLA and PKU-MMD I datasets demonstrate significant improvements in distinguishing ambiguous actions. Our code will be made available at https://github.com/HaoHuang2003/SFHead.