50 citations · 105 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
Lingdong Kong, Yaru Niu, Shaoyuan Xie +39
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical a…
Optical flow estimation from event-based cameras and spiking neural networks
Javier Cuadrado, Ulysse Rançon, Benoît Cottereau +2
Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or "spikes", when the luminance chan…
Efficient visual object representation using a biologically plausible spike-latency code and winner-take-all inhibition
Melani Sanchez-Garcia, Tushar Chauhan, Benoit R. Cottereau +1
Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contras…
StereoSpike: Depth Learning with a Spiking Neural Network
Ulysse Rançon, Javier Cuadrado-Anibarro, Benoit R. Cottereau +1
Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics. Here we solved it using a…