The spiking neural network (SNN), as a promising brain-inspired computational model with
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode
information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

We focus on a very challenging task: imaging at nighttime dynamic scenes. Most previous
methods rely on the low-light enhancement of a conventional RGB camera. However they …

Event-based semantic segmentation (ESS) is a fundamental yet challenging task for event
camera sensing. The difficulties in interpreting and annotating event data limit its scalability …

Recently, some researchers have begun to adopt the Transformer to combine or replace the
widely used ResNet as their new backbone network. As the Transformer captures the long …

Event cameras characterized by high temporal resolution high dynamic range low power
consumption and high pixel bandwidth offer unique capabilities for object detection in …

Spiking neural network (SNN) has been viewed as a potential candidate for the next
generation of artificial intelligence with appealing characteristics such as sparse …

The main streams of human activity recognition (HAR) algorithms are developed based on
RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and …