3 papers
cs.CV2026
Toward On-Chip Training of Spiking Neural Networks for Dense Event-Based Vision
Maxime Vaillant, Axel Carlier, Lai Xing Ng +2
Event cameras provide low-latency, asynchronous visual sensing for resource-constrained robotics. Spiking neural networks (SNNs) process event streams naturally, but training deep…
cs.CV2026
Object-Level Explanations for Image Geolocation Models: a GeoGuessr use-case
Emilie Durrieu, Christophe Hurter, Philippe Muller +1
When humans play geolocation games such as GeoGuessr, they rely on concrete visual cues, such as road markings, vegetation, or architectural details, to infer where an image was ca…
cs.CV2026
SpikeCLR: Contrastive Self-Supervised Learning for Few-Shot Event-Based Vision using Spiking Neural Networks
Maxime Vaillant, Axel Carlier, Lai Xing Ng +2
Event-based vision sensors provide significant advantages for high-speed perception, including microsecond temporal resolution, high dynamic range, and low power consumption. When…