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Zero Shot Skeletal Action Recognition On Ntu 1

Metrics

Accuracy (10 unseen classes)

Accuracy (24 unseen classes)

Results

Performance results of various models on this benchmark

			Paper Title	Repository
TDSM	74.15	65.06	TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition
PURLS	71.95	52.01	Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
SA-DVAE	68.77	46.12	SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders
SMIE	65.74	45.30	Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization
STAR	63.30	44.30	Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition	-
SynSE	62.69	38.70	Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition
CADA-VAE	59.53	35.77	Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
SA-DVAE + augmented text	-	-	SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders

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Zero Shot Skeletal Action Recognition On Ntu 1 | SOTA | HyperAI