Abstract We present Recurrent Vision Transformers (RVTs), a novel backbone for object
detection with event cameras. Event cameras provide visual information with sub …

The best performing learning algorithms devised for event cameras work by first converting
events into dense representations that are then processed using standard CNNs. However …

Event cameras are a type of novel neuromorphic sen-sor that has been gaining increasing
attention. Existing event-based backbones mainly rely on image-based designs to extract …

Automotive embedded algorithms have very high constraints in terms of latency, accuracy
and power consumption. In this work, we propose to train spiking neural networks (SNNs) …

Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μ s
level temporal resolution, has prominent advantages in many specific visual scenarios and …

Event cameras, with their high dynamic range and temporal resolution, are ideally suited for
object detection in scenarios with motion blur and challenging lighting conditions. However …

Event cameras (EC) represent a paradigm shift and are emerging as valuable tools in the
automotive industry, particularly for in-cabin and out-of-cabin monitoring. These cameras …

Abstract Today, state-of-the-art deep neural networks that process events first convert them
into dense, grid-like input representations before using an off-the-shelf network. However …

Recently, event cameras have shown large applicability in several computer vision fields
especially concerning tasks that require high temporal resolution. In this work, we …

Event-based cameras are novel vision sensors that respond to local variations in intensity,
generating asynchronous pixels, referred to as events, with low latency, high temporal …