Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation

Core Concepts

The proposed knowledge distillation framework distills discriminative part-level knowledge from heterogeneous high-quality skeletons to enhance representations of low-quality skeletons, enabling accurate action recognition even in the presence of intensive noise.

Abstract

The paper addresses the challenge of skeleton-based action recognition using low-quality skeleton data, which often contains missing or inaccurate joints. The authors propose a general knowledge distillation framework that employs a teacher-student model setup. The teacher model is pre-trained on high-quality skeletons, while the student model handles low-quality skeletons.
To bridge the gap between heterogeneous high-quality and low-quality skeletons, the authors present a novel part-based skeleton matching strategy. This strategy exploits shared body parts to facilitate local action pattern learning. An action-specific part matrix is developed to emphasize critical parts for different actions, enabling the student model to distill discriminative part-level knowledge.
Furthermore, a novel part-level multi-sample contrastive loss is introduced to achieve knowledge transfer from multiple high-quality skeletons to low-quality ones. This allows the proposed framework to include training low-quality skeletons that lack corresponding high-quality matches.
Comprehensive experiments on the NTU-RGB+D, Penn Action, and SYSU 3D HOI datasets demonstrate the effectiveness of the proposed knowledge distillation framework in enhancing action recognition performance using low-quality skeleton data.

Stats

The teacher model trained on high-quality skeletons achieves 84.56% and 89.46% accuracy on the NTU-RGB+D dataset, 90.54% Top1 and 98.97% Top5 accuracy on the Penn Action dataset, and 88.54% and 88.96% accuracy on the SYSU 3D HOI dataset.
The student model trained on low-quality skeletons without knowledge distillation achieves 79.98% and 84.88% accuracy on the NTU-RGB+D dataset, 83.43% Top1 and 97.19% Top5 accuracy on the Penn Action dataset, and 84.33% and 84.00% accuracy on the SYSU 3D HOI dataset.
With the proposed knowledge distillation framework, the student model achieves 83.31% and 88.13% accuracy on the NTU-RGB+D dataset, 87.08% Top1 and 98.50% Top5 accuracy on the Penn Action dataset, and 87.14% and 86.68% accuracy on the SYSU 3D HOI dataset.

Quotes

"The proposed framework employs a teacher-student model setup, where a teacher model trained on high-quality skeletons guides the learning of a student model that handles low-quality skeletons."
"To bridge the gap between heterogeneous high-quality and low-quality skeletons, we present a novel part-based skeleton matching strategy, which exploits shared body parts to facilitate local action pattern learning."
"A novel part-level multi-sample contrastive loss achieves knowledge transfer from multiple high-quality skeletons to low-quality ones, which enables the proposed knowledge distillation framework to include training low-quality skeletons that lack corresponding high-quality matches."

Key Insights Distilled From

Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation

by Cuiwei Liu,Y... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18206.pdf

Deeper Inquiries

How can the proposed part-based skeleton matching strategy be extended to handle more complex skeleton representations, such as those with varying numbers of joints or different joint connections

The proposed part-based skeleton matching strategy can be extended to handle more complex skeleton representations by incorporating adaptive mechanisms to account for varying numbers of joints or different joint connections. One approach could involve dynamically adjusting the grouping of joints into parts based on the specific characteristics of the skeleton data. For skeletons with varying numbers of joints, the strategy could involve a flexible grouping mechanism that adapts to the available joints in each instance. This adaptability could be achieved through hierarchical clustering algorithms that group joints based on proximity or functional relationships. Additionally, for skeletons with different joint connections, the strategy could incorporate graph matching techniques to align joints with similar functions across different representations. By leveraging advanced graph matching algorithms, the strategy can effectively match corresponding joints and parts even in the presence of varying joint connections.

What other types of knowledge, beyond part-level features, could be distilled from high-quality skeletons to further improve the performance of the student model on low-quality skeletons

Beyond part-level features, several other types of knowledge could be distilled from high-quality skeletons to further enhance the performance of the student model on low-quality skeletons. Some potential knowledge types include:

Temporal Dynamics: Extracting temporal patterns and motion sequences from high-quality skeletons to improve the temporal modeling capabilities of the student model.
Spatial Relationships: Distilling information about the spatial relationships between joints and body parts to enhance the spatial understanding of the student model.
Action Context: Incorporating contextual information about actions, such as common action sequences or transitions, to improve the action recognition accuracy of the student model.
Error Correction: Transferring knowledge about common error patterns or noise characteristics in low-quality skeletons to enable the student model to correct inaccuracies and missing data effectively.
Attention Mechanisms: Leveraging attention mechanisms to focus on critical joints or parts during action recognition, enhancing the model's ability to capture important action cues.

By integrating these additional types of knowledge into the knowledge distillation framework, the student model can benefit from a more comprehensive and robust learning process, leading to improved performance on low-quality skeleton data.

How can the proposed knowledge distillation framework be adapted to work with other modalities, such as RGB videos or depth maps, to enhance action recognition in real-world scenarios with limited high-quality data

To adapt the proposed knowledge distillation framework to work with other modalities such as RGB videos or depth maps for action recognition, several modifications and enhancements can be implemented:

Feature Extraction: Utilize convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to extract features from RGB videos or depth maps, capturing spatial and temporal information relevant to action recognition.
Modalities Fusion: Develop fusion strategies to combine features extracted from different modalities, enabling the model to leverage complementary information for improved performance.
Knowledge Transfer: Apply knowledge distillation techniques to transfer knowledge from high-quality RGB videos or depth maps to low-quality counterparts, enhancing the learning process for the student model.
Multi-Modal Learning: Implement multi-modal learning approaches to jointly train the model on multiple modalities, allowing for a more comprehensive understanding of actions and improving generalization capabilities.
Data Augmentation: Incorporate data augmentation techniques specific to RGB videos or depth maps to enhance the model's robustness to variations in input data.

By adapting the knowledge distillation framework to accommodate different modalities, the model can effectively leverage diverse sources of information to enhance action recognition in real-world scenarios with limited high-quality data.