Neural events compress event camera streams into fewer informative tokens via discrete asynchronous autoencoders, achieving on-par or better performance on detection and classification with 2x lower event rate.
A 128× 128 120 db 15 µs latency asynchronous temporal contrast vision sensor.IEEE Journal of Solid-State Circuits 2008; 43(2): 566–576
5 Pith papers cite this work. Polarity classification is still indexing.
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Presents the ev-CIVIL dataset and benchmark showing that event-based cameras can support real-time detection of cracks and spalling in civil infrastructure under challenging lighting.
GeoIMO uses a yaw-compensated focus of expansion model on event streams to classify independent object motion via scale-invariant residuals without training or labels.
Multi-stage silicon retina on SCAMP-5 achieves 13% lower saliency prediction loss and 47% fewer events than standard DVS using a ~100k-parameter network.
A coarse-to-fine autoregressive framework with multi-scale tokenization and scale-aware control reconstructs human motion from sparse observations and reports SOTA accuracy on AMASS.
citing papers explorer
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Neural Events: Discrete Asynchronous Autoencoders for Event-Based Vision
Neural events compress event camera streams into fewer informative tokens via discrete asynchronous autoencoders, achieving on-par or better performance on detection and classification with 2x lower event rate.
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Event-based Civil Infrastructure Visual Defect Detection: ev-CIVIL Dataset and Benchmark
Presents the ev-CIVIL dataset and benchmark showing that event-based cameras can support real-time detection of cracks and spalling in civil infrastructure under challenging lighting.
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GeoIMO: Geometry-Driven Independent Motion Classification for Event Cameras
GeoIMO uses a yaw-compensated focus of expansion model on event streams to classify independent object motion via scale-invariant residuals without training or labels.
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MotionMAR: Multi-scale Auto-Regressive Human Motion Reconstruction from Sparse Observations
A coarse-to-fine autoregressive framework with multi-scale tokenization and scale-aware control reconstructs human motion from sparse observations and reports SOTA accuracy on AMASS.