Unsupervised spiking feature learning for event-based pedestrian crossing detection: approaching supervised accuracy without labelled training data

arXiv:2609.31671 · cs.CV, cs.AI, cs.NE · Submitted 2026-09-15 · Read on arXiv

cs.CV, cs.AI, cs.NE

Submitted: 2026-09-15

Updated: 2026-09-15

Terminology

• AUROC — 47× in this paper · explained in 2 episodes
• Benchmark — 28× in this paper · explained in 2 episodes
• Accuracy — 22× in this paper · explained in 1 episode
• Adaptation — 5× in this paper · explained in 1 episode
• Robustness — 4× in this paper · explained in 5 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 3× in this paper · explained in 4 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Linear probe — 2× in this paper · explained in 2 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Spiking Neural Networks (SNNs) — 1× in this paper · explained in 3 episodes

Sources

• Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation
• A Large Scale Event-based Detection Dataset for Automotive

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