SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition (2403.09508v3)

Abstract: Skeleton-based action recognition, which classifies human actions based on the coordinates of joints and their connectivity within skeleton data, is widely utilized in various scenarios. While Graph Convolutional Networks (GCNs) have been proposed for skeleton data represented as graphs, they suffer from limited receptive fields constrained by joint connectivity. To address this limitation, recent advancements have introduced transformer-based methods. However, capturing correlations between all joints in all frames requires substantial memory resources. To alleviate this, we propose a novel approach called Skeletal-Temporal Transformer (SkateFormer) that partitions joints and frames based on different types of skeletal-temporal relation (Skate-Type) and performs skeletal-temporal self-attention (Skate-MSA) within each partition. We categorize the key skeletal-temporal relations for action recognition into a total of four distinct types. These types combine (i) two skeletal relation types based on physically neighboring and distant joints, and (ii) two temporal relation types based on neighboring and distant frames. Through this partition-specific attention strategy, our SkateFormer can selectively focus on key joints and frames crucial for action recognition in an action-adaptive manner with efficient computation. Extensive experiments on various benchmark datasets validate that our SkateFormer outperforms recent state-of-the-art methods.

• The paper presents SkateFormer as a novel skeletal-temporal transformer model that partitions joints and frames for enhanced action recognition performance.
• It introduces partition-specific attention (Skate-MSA) and Skate-Embedding techniques to efficiently capture spatial and temporal dependencies in human movements.
• Experimental results on NTU RGB+D, NTU RGB+D 120, and NW-UCLA show that SkateFormer outperforms state-of-the-art models in recognizing complex human interactions.

Skeletal-Temporal Transformer: Advances in Action Recognition

The paper "SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition" introduces a novel approach for skeleton-based action recognition, addressing the limitations present in traditional Graph Convolutional Networks (GCNs) while optimizing computational efficiency in transformer-based methods. The authors propose an innovative method with the introduction of SkateFormer, which utilizes a Skeletal-Temporal Transformer framework and elaborates on partition-specific attention to significantly enhance performance on human action recognition tasks.

Overview of the Methodology

The essence of SkateFormer lies in its ability to partition the joints and frames of skeleton sequences into semantically meaningful types, leveraging both spatial and temporal relationships intrinsic to human movements. The authors define four distinct skeletal-temporal relation types, termed Skate-Types:

1. Neighboring joints with local motion,
2. Distant joints with local motion,
3. Neighboring joints with global motion,
4. Distant joints with global motion.

By applying skeletal-temporal self-attention specifically tailored to each partition, SkateFormer adeptly captures context-specific dependencies without resorting to the computationally expensive approach of full self-attention across all joint-frame pairs.

SkateFormer employs a partition-specific attention strategy dubbed Skate-MSA, which stands out for its ability to switch focus efficiently among the defined skeletal-temporal partitions. This strategy enables the model to balance between computational load and complexity. Furthermore, SkateFormer introduces a new method called Skate-Embedding for positional encoding, which forms an outer product between learnable skeletal features and fixed temporal index features, further boosting its action recognition performance.

Experimental Validation and Results

The paper presents detailed experimental validation conducted across several benchmark datasets, including NTU RGB+D, NTU RGB+D 120, and NW-UCLA. Results consistently show that SkateFormer outperforms state-of-the-art models in terms of action recognition accuracy. On average, the proposed model surpasses other approaches even when evaluated with single modalities, achieving notably higher performance gains for complex human interaction categories, which historically have been challenging for previous models that rely on individual modality performance.

Implications and Future Directions

The methodological contributions outlined in the paper assert the significance of tailoring attention mechanisms to specific spatiotemporal structures within skeletal data. By exploring partition-specific attention strategies, SkateFormer demonstrates how focusing on distinctive types of skeletal-temporal relations enhances the discriminative power of action classifiers, making it exceptionally robust for real-time applications where computational efficiency is paramount.

As AI and machine learning continue to advance, SkateFormer's contribution could seed further exploration into multi-level partitioning strategies in other domains of computer vision and robotics. Extending this methodology could aid in understanding not only actions but finer granularity tasks such as gesture or emotion recognition. Additionally, incorporating such location-aware attention mechanisms might provide insights into optimizing broader transformer architectures for various sensory data integration tasks.

In conclusion, SkateFormer introduces a highly efficient yet powerful framework for skeleton-based action recognition, linking between precise attention mechanisms and enhanced action classification. As recognized by its experimental success, the innovative use of partition-specific strategies in the temporal domain serves as a promising direction towards more proficient models in the AI field.