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HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification

CVPR 2018 · Detection and segmentation › CNN features · approach: Spiking event-camera detection · 498 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use HATS instead of HOTSEvent-based object classification · N-MNISTaccuracy: 0.808 -> 0.991Helped
Use HATS instead of HOTSEvent-based object classification · N-Caltech101accuracy: 0.210 -> 0.642Helped
Use HATS instead of HOTSEvent-based object classification · MNIST-DVSaccuracy: 0.803 -> 0.984Helped
Use HATS instead of HOTSEvent-based object classification · CIFAR10-DVSaccuracy: 0.271 -> 0.524Helped
Use HATS instead of Gabor-SNNEvent-based object classification · N-CARSaccuracy: 0.789 -> 0.902Helped
Use HATS instead of Gabor-SNNEvent-based object classification · N-CARSAUC: 0.735 -> 0.945Helped
Use HATS instead of HOTSEvent-based feature extraction · N-CARSaverage computation time per sample: 157.57 -> 7.28Helped
Use HATS instead of Gabor-SNNEvent-based feature extraction · N-CARSaverage computation time per sample: 285.95 -> 7.28Helped
Add a local-memory time surface that accumulates a temporal window of past events instead of using only the last event (ablation)HATS representation · Object classificationaccuracyHelped
Add block normalization (ablation)HATS on N-CARS · N-CARSaccuracyNo clear effect
Use smaller cell size K (ablation)HATS · Low-resolution inputsaccuracyHelped

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