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Zero Shot Skeletal Action Recognition On Ntu

Metrics

Accuracy (12 unseen classes)
Accuracy (5 unseen classes)
Random Split Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy (12 unseen classes)
Accuracy (5 unseen classes)
Random Split Accuracy
Paper TitleRepository
SA-DVAE41.3882.3784.20SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders-
SA-DVAE + augmented text--87.61SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders-
PURLS40.9979.23-Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
STAR45.1081.4077.50Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition-
TDSM56.0386.4988.88TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition
SMIE40.1877.9865.08Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization
CADA-VAE28.9676.8460.74Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
SynSE33.3075.8164.19Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition
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