5 papers
Bend the Clock: Predicting Ahead to Beat Latency in Event-Based Object Detection
Biswadeep Sen, Benoit R. Cottereau, Nicolas Cuperlier +1
Event cameras promise low-latency perception for high-speed robotic systems, where even short delays can render detections stale by the time they inform downstream robotic decision…
REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception
Geoffroy Keime, Nicolas Cuperlier, Benoit R. Cottereau
Robotic systems operating in dynamic environments require visual perception that evolves continuously with the incoming sensory stream. Event cameras provide microsecond temporal r…
Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition
Geoffroy Keime, Nicolas Cuperlier, Benoit R. Cottereau
Reliable visual place recognition (VPR) under dynamic real-world conditions is critical for autonomous robots, yet conventional deep networks remain limited by high computational a…
E-TIDE: Fast, Structure-Preserving Motion Forecasting from Event Sequences
Biswadeep Sen, Benoit R. Cottereau, Nicolas Cuperlier +1
Event-based cameras capture visual information as asynchronous streams of per-pixel brightness changes, generating sparse, temporally precise data. Compared to conventional frame-b…
Forming a sparse representation for visual place recognition using a neurorobotic approach
Sylvain Colomer, Nicolas Cuperlier, Guillaume Bresson +1
This paper introduces a novel unsupervised neural network model for visual information encoding which aims to address the problem of large-scale visual localization. Inspired by th…