PartialVisGraph is a hypergraph framework with learnable virtual hyperedges and a sample-adaptive transformer incorporating visibility prior, achieving reported SOTA gains up to 68.8% under simulated partial FoV on NTU RGB+D datasets.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
2 Pith papers cite this work. Polarity classification is still indexing.
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Neurosymbolic framework detects seizures from video skeletons by activating clinical concepts and composing them with differentiable logic into interpretable rules, evaluated on two benchmarks with public code release.
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Partial Skeleton Visibility for Action Recognition: A Constrained Field-of-View Approach
PartialVisGraph is a hypergraph framework with learnable virtual hyperedges and a sample-adaptive transformer incorporating visibility prior, achieving reported SOTA gains up to 68.8% under simulated partial FoV on NTU RGB+D datasets.
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A Neurosymbolic Framework for Interpretable Skeleton-Based Seizure Detection via Concept-Driven Logical Reasoning
Neurosymbolic framework detects seizures from video skeletons by activating clinical concepts and composing them with differentiable logic into interpretable rules, evaluated on two benchmarks with public code release.