Zero Shot Skeletal Action Recognition On Ntu
评估指标
Accuracy (12 unseen classes)
Accuracy (5 unseen classes)
Random Split Accuracy
评测结果
各个模型在此基准测试上的表现结果
模型名称 | Accuracy (12 unseen classes) | Accuracy (5 unseen classes) | Random Split Accuracy | Paper Title | Repository |
|---|---|---|---|---|---|
| SA-DVAE | 41.38 | 82.37 | 84.20 | SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders | - |
| SA-DVAE + augmented text | - | - | 87.61 | SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders | - |
| PURLS | 40.99 | 79.23 | - | Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition | |
| STAR | 45.10 | 81.40 | 77.50 | Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition | - |
| TDSM | 56.03 | 86.49 | 88.88 | TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition | |
| SMIE | 40.18 | 77.98 | 65.08 | Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization | |
| CADA-VAE | 28.96 | 76.84 | 60.74 | Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders | |
| SynSE | 33.30 | 75.81 | 64.19 | Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition |
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