Ev-DTAD improves event-based object detection accuracy and speed by using hierarchical temporal aggregation at the representation level and frequency-aware hypergraph fusion at the model level.
Hypergcn: A new method for training graph convolutional networks on hypergraphs
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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HND models hypergraph feature propagation as an anisotropic diffusion process governed by a continuous-time PDE, discretized into stable neural layers with energy dissipation and boundedness guarantees.
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Rethinking Event-Based Object Dtection through Representation-Level Temporal Aggregation and Model-Level Hypergraph Reasoning
Ev-DTAD improves event-based object detection accuracy and speed by using hierarchical temporal aggregation at the representation level and frequency-aware hypergraph fusion at the model level.
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Hypergraph Neural Diffusion: A PDE-Inspired Framework for Hypergraph Message Passing
HND models hypergraph feature propagation as an anisotropic diffusion process governed by a continuous-time PDE, discretized into stable neural layers with energy dissipation and boundedness guarantees.