MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion (2409.10473v1)

Abstract: Self-supervised learning has proved effective for skeleton-based human action understanding. However, previous works either rely on contrastive learning that suffers false negative problems or are based on reconstruction that learns too much unessential low-level clues, leading to limited representations for downstream tasks. Recently, great advances have been made in generative learning, which is naturally a challenging yet meaningful pretext task to model the general underlying data distributions. However, the representation learning capacity of generative models is under-explored, especially for the skeletons with spacial sparsity and temporal redundancy. To this end, we propose Masked Conditional Diffusion (MacDiff) as a unified framework for human skeleton modeling. For the first time, we leverage diffusion models as effective skeleton representation learners. Specifically, we train a diffusion decoder conditioned on the representations extracted by a semantic encoder. Random masking is applied to encoder inputs to introduce a information bottleneck and remove redundancy of skeletons. Furthermore, we theoretically demonstrate that our generative objective involves the contrastive learning objective which aligns the masked and noisy views. Meanwhile, it also enforces the representation to complement for the noisy view, leading to better generalization performance. MacDiff achieves state-of-the-art performance on representation learning benchmarks while maintaining the competence for generative tasks. Moreover, we leverage the diffusion model for data augmentation, significantly enhancing the fine-tuning performance in scenarios with scarce labeled data. Our project is available at https://lehongwu.github.io/ECCV24MacDiff/.

• The paper introduces a unified framework that employs a masked conditional diffusion model to overcome challenges in self-supervised skeleton-based action recognition.
• It integrates generative and discriminative techniques to achieve state-of-the-art results on benchmarks like NTU RGB+D and PKUMMD.
• The approach leverages diffusion-based data augmentation to enhance fine-tuning performance, especially in scenarios with limited labeled data.

Overview of "MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion"

The paper "MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion" introduces an innovative approach to leveraging diffusion models for self-supervised learning of skeleton-based human actions. The authors scrutinize the limitations of prior self-supervised methods, such as contrastive learning, which struggles with false negatives, and reconstruction models, which may capture low-level features irrelevant for high-level understanding. The proposed method, Masked Conditional Diffusion (MacDiff), aims to overcome these hurdles by synthesizing the strengths of generative and contrastive objectives.

Key Contributions

1. Masked Conditional Diffusion (MacDiff) Framework: The paper presents a unified framework for skeletal representation that leverages a diffusion-based generative model. This involves a novel integration of an encoder-decoder architecture with a focus on skeleton data, addressing its inherent spatial sparsity and temporal redundancy.
2. Theoretical Insights: The authors provide a theoretical analysis from a mutual information perspective, showing how the proposed generative objective improves upon traditional contrastive learning approaches by enriching representation learning with more downstream task-relevant information.
3. Generative and Discriminative Competency: MacDiff achieves state-of-the-art results in both representation learning benchmarks and generative tasks. The framework is designed not just for discriminative applications but also for synthesizing realistic skeleton sequences, indicating the model's robustness across multiple tasks.
4. Diffusion-based Data Augmentation: The model leverages its generative capabilities to augment datasets, especially useful in scarce labeled data scenarios. This approach significantly enhances fine-tuning performance, demonstrating practical relevance in real-world applications where data labeling is challenging and costly.

Numerical Results and Implications

The paper validates its claims with numerous experiments across large-scale datasets, such as NTU RGB+D 60, NTU RGB+D 120, and PKUMMD. MacDiff not only surpasses existing methods like SkeletonMAE and various contrastive techniques but also exhibits superior transfer learning capabilities. For instance, the model's application in semi-supervised settings shows marked improvements, capitalizing on its diffusion-based data augmentation strategy. Such results suggest a promising avenue for further explorations in leveraging generative models for self-supervised learning in skeletal action recognition.

Future Directions

The framework laid out by MacDiff invites several speculative paths for future research. One pertinent area is the exploration of the generalization capacity of MacDiff in diverse environments with varying noise levels. Additionally, refining the diffusion model's structure or incorporating more sophisticated conditioning methods could potentially enhance the representation and generation balance, expanding the feasibility of these models in other domains, such as fine motor activity recognition and beyond.

In conclusion, this paper introduces a significant advancement in skeleton-based action modeling, setting a new benchmark for self-supervised learning frameworks by intelligently merging contrastive and generative principles. As diffusion models continue to gain traction, methodologies such as MacDiff exemplify how these models can innovate current paradigms in computer vision and machine learning.