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Skeleton based Zero Shot Action Recognition in Joint Pose-Language Semantic Space

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arxiv 1911.11344 v1 pith:SLPMP4BG submitted 2019-11-26 cs.CV

Bhavan Jasani , Afshaan Mazagonwalla This is my paper · ORCID
classification cs.CV
keywords actionmodelseenshotzerobeforeclasscorrectly
verification ladder T0 review T1 audit T2 compute T3 formal
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How does one represent an action? How does one describe an action that we have never seen before? Such questions are addressed by the Zero Shot Learning paradigm, where a model is trained on only a subset of classes and is evaluated on its ability to correctly classify an example from a class it has never seen before. In this work, we present a body pose based zero shot action recognition network and demonstrate its performance on the NTU RGB-D dataset. Our model learns to jointly encapsulate visual similarities based on pose features of the action performer as well as similarities in the natural language descriptions of the unseen action class names. We demonstrate how this pose-language semantic space encodes knowledge which allows our model to correctly predict actions not seen during training.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GenPrior: Unleashing Text-to-Motion Generative Priors for Zero-Shot Skeleton-based Action Recognition

    cs.CV 2026-08 conditional novelty 7.0 of 10

    Using text-to-motion generation to inject kinematic structure into text prototypes improves zero-shot skeleton action recognition, though most of the reported gain comes from test-time prototype self-refinement.

  2. Zero-Shot Skeleton-Based Action Recognition With Prototype-Guided Feature Alignment

    cs.CV 2025-07 conditional novelty 5.0 of 10

    PGFA improves zero-shot skeleton action recognition via end-to-end contrastive training and test-time prototype-based reclassification, reporting large gains on NTU-60, NTU-120, and PKU-MMD.

  3. Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition

    cs.CV 2024-11 conditional novelty 5.0 of 10

    Neuron uses multi-turn LLM descriptions and evolving spatial-temporal micro-prototypes to improve zero-shot skeleton action recognition.

  4. Automated Classification of Cybercrime Complaints using Transformer-based Language Models for Hinglish Texts

    cs.CR 2024-12 conditional novelty 4.0 of 10

    HingRoBERTa, fine-tuned on augmented Hinglish cybercrime complaints, reaches 74.41% accuracy and 71.49% F1, outperforming generic BERT/RoBERTa and TF-IDF baselines.

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