DiMoP: Diffusion-Driven Motion Representation Learning With Frame-Level Pseudo-Classification for Skeleton-Based Action Recognition

arXiv:2609.35444 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Transformer — 18× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Representation Learning — 15× in this paper · explained in 2 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• Embeddings — 9× in this paper · explained in 2 episodes
• Latent Representation — 9× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Transfer Learning — 5× in this paper · explained in 10 episodes
• Contrastive Learning — 4× in this paper · explained in 5 episodes
• Downstream Tasks — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Latent Representations — 4× in this paper · explained in 3 episodes
• Pseudo-labels — 4× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Diffusion Process — 3× in this paper · explained in 2 episodes
• Homogeneity — 3× in this paper · explained in 2 episodes
• Self-Supervised Learning — 3× in this paper · explained in 8 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Advanced Multimedia Research Lab — 2× in this paper
• Generative Model — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Action Groups — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 1× in this paper
• Clustering — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• Masked Autoencoder (MAE) — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Hypergraph Transformer for Skeleton-based Action Recognition
• Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition

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