Neural Events: Discrete Asynchronous Autoencoders for Event-Based Vision

• URL: http://arxiv.org/abs/2606.19835v1
• Date: Thu, 18 Jun 2026 06:26:25 GMT
• Title: Neural Events: Discrete Asynchronous Autoencoders for Event-Based Vision
• Abstract summary: Event cameras capture dynamic scenes with exceptional temporal fidelity.<n>To derive meaningful signals, downstream algorithms need to quickly integrate from a potentially massive torrent of low-information events.<n>This paper proposes a framework to retoken event streams into small set of highly informative textitneural events.
• Score: 33.23552175352345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event cameras capture dynamic scenes with exceptional temporal fidelity by representing them as a continuous stream of microsecond resolution \textit{events}. Each individual event, however, only carries minimal semantic value, merely signaling a localized brightness change. To derive meaningful signals, downstream algorithms need to quickly integrate cues from a potentially massive torrent of low-information events. Current architectures, however, are easily overwhelmed, struggling to balance capturing fine-grained temporal dynamics and maintaining a manageable data throughput. This paper proposes a framework to re-tokenize event streams into a small set of highly informative \textit{neural events}, each representing a local spatio-temporal context window with a discrete learnable code. Every time this code flips, a neural event is triggered, yielding a highly compressed data stream. We demonstrate that, across object detection and classification, networks trained on neural events are on par or surpass the performance of state-of-the-art approaches while reducing the event rate by a factor of 2.0.

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