Action Transformer: A self-attention model for short-time pose-based human action recognition
Results and benchmarks
Action Transformer: A self-attention model for short-time pose-based human action recognition presents a transformer approach for computer science.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
Use the implementation status and reproduction sections for the current action plan.
No verified maintained repo yet
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
- No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
- Start from related paper: Action Transformer: A Self-Attention Model for Short-Time Human Action Recognition..
- Start from this likely method family: Transformer.
Time to first repro: a few days
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- Estimate is based on paper-only reproduction flow
Reproduction readiness
No repo
No verified implementation available
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Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Framework baselines
- Hugging Face Transformers training guide
Modern transformer training baseline.
- PyTorch nn.Transformer docs
Reference transformer building block implementation.
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.
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Research context
276
Citations
73
References
Tasks
Computer science, Convolutional neural network, Exploit, Action recognition, Benchmark (surveying), Generalization, Deep learning, human action recognition
Methods
Transformer, Architecture, self-attention
Domains
Artificial intelligence, Machine learning, Action (physics), Computer Vision and Pattern Recognition
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