Zero Shot Skeletal Action Recognition
6 papers with code • 3 benchmarks • 3 datasets
Zero-Shot Learning for 3D Skeleton-Based Action Recognition
NTU RGB+D
NTU RGB+D 120
PKU-MMD
Most implemented papers
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
edgarschnfld/CADA-VAE-PyTorch
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Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.
Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition
We deploy SynSE for the task of skeleton-based action sequence recognition.
Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization
yujieouo/smie
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Specifically, 1) we maximize the MI between visual and semantic space for distribution alignment; 2) we leverage the temporal information for estimating the MI by encouraging MI to increase as more frames are observed.
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
azzh1/purls
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CVPR 2024
While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored.
SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders
Existing zero-shot skeleton-based action recognition methods utilize projection networks to learn a shared latent space of skeleton features and semantic embeddings.
TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition
In zero-shot skeleton-based action recognition, aligning skeleton features with the text features of action labels is essential for accurately predicting unseen actions.
