FATE combines pillar encoding via orthogonal polynomial basis with frequency-aware training to enable event-based object detection at up to 200 Hz without internal temporal sub-binning.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
SNNs deployed on Loihi 2 achieve real-time object detection with the lowest dynamic energy per inference and recover 87-100% of ANN accuracy via distillation-aware training.
citing papers explorer
-
FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection
FATE combines pillar encoding via orthogonal polynomial basis with frequency-aware training to enable event-based object detection at up to 200 Hz without internal temporal sub-binning.
-
Real-Time Frame- and Event-based Object Detection with Spiking Neural Networks on Edge Neuromorphic Hardware: Design, Deployment and Benchmark
SNNs deployed on Loihi 2 achieve real-time object detection with the lowest dynamic energy per inference and recover 87-100% of ANN accuracy via distillation-aware training.