Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition (1904.01189v3)

Abstract: Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data. Recently, there is a trend of using very deep feedforward neural networks to model the 3D coordinates of joints without considering the computational efficiency. In this paper, we propose a simple yet effective semantics-guided neural network (SGN) for skeleton-based action recognition. We explicitly introduce the high level semantics of joints (joint type and frame index) into the network to enhance the feature representation capability. In addition, we exploit the relationship of joints hierarchically through two modules, i.e., a joint-level module for modeling the correlations of joints in the same frame and a framelevel module for modeling the dependencies of frames by taking the joints in the same frame as a whole. A strong baseline is proposed to facilitate the study of this field. With an order of magnitude smaller model size than most previous works, SGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets. The source code is available at https://github.com/microsoft/SGN.

• The paper introduces the SGN framework that leverages joint and frame-level semantics to improve recognition accuracy and efficiency compared to conventional methods.
• It employs distinct joint-level and frame-level modules to effectively capture spatial relationships and temporal dynamics in skeletal data.
• Empirical results on NTU60, NTU120, and SYSU demonstrate SGN’s lightweight design and superior performance over RNN, CNN, and GCN approaches.

Overview of Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition

The paper "Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition" introduces a novel approach for recognizing human actions using skeletal data with an emphasis on the semantic information inherent in the structure of human joints. This research is situated within the context of skeleton-based human action recognition, a rapidly advancing field due to the structured nature of skeletal data which provides robustness against variations in appearance and viewpoint.

In this work, the authors propose the Semantics-Guided Neural Network (SGN), which is designed to leverage high-level semantic details, explicitly incorporating joint type and frame index information to enhance the model's representational power. The SGN contrasts with standard feedforward architectures which typically overlook semantic information, resulting in models that may be computationally intensive without a corresponding improvement in performance.

Key Contributions

The SGN framework introduces several innovative components and strategies:

1. Joint-Level and Frame-Level Modules: The architecture is hierarchically structured with distinct modules to capture correlations at multiple levels. The joint-level module models the relationships among joints within a frame, while the frame-level module addresses dependencies across frames by treating joints collectively.
2. Explicit Semantic Utilization: By incorporating joint type and frame index directly into the network's inputs, the SGN allows for more effective learning of spatial and temporal dependencies. This explicit inclusion helps to guide the network in capturing the natural order and significance of joints, facilitating better recognition tasks.
3. Strong Baseline Development: SGN also establishes a robust baseline model that outperforms numerous existing methods both in accuracy and efficiency. This baseline is constructed to be lightweight compared to traditional approaches that are heavily parameterized, offering a more efficient alternative without sacrificing performance.

Performance and Implications

Empirical evaluations reveal that SGN achieves state-of-the-art results on benchmark datasets such as NTU60, NTU120, and SYSU. It outperforms many skeleton-based action recognition methods, including those leveraging RNNs, CNNs, and GCNs. The advantages are particularly significant given the smaller model size, which is an order of magnitude less than several competing models, marking a noteworthy achievement in balancing performance capabilities with computational requirements.

The results underscore the potential impact of utilizing semantics in optimizing network architectures for action recognition tasks. Practically, the SGN framework can be applied in areas such as human-computer interaction, surveillance, and video retrieval, where action recognition plays a pivotal role. Theoretically, the paper provokes further investigation into the integration of semantic information in neural networks and highlights the convergence of semantic interpretation and model efficiency, paving the way for future research directions in skeletal data analysis and machine learning architectures.

Overall, this research demonstrates the significant benefits of semantics in designing efficient neural networks, presenting valuable methodologies for exploiting structural data in complex recognition systems. The release of the SGN source code also invites broader adoption and experimentation within the research community.