This paper proposes a low latency neural network architecture for event-based dense
prediction tasks. Conventional architectures encode entire scene contents at a fixed rate …

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 …

For vehicles to navigate autonomously, they need to perceive and understand their
immediate surroundings. Currently, cameras are the preferred sensors, due to their high …

Object detection with event cameras benefits from the sensor's low latency and high
dynamic range. However it is costly to fully label event streams for supervised training due to …

Image restoration refers to enhance the visibility of degraded images. Given the complex
and variable aspects of image degradation, current methods tend to sacrifice contextual …

As a computational technology, single-pixel microscopic imaging (SPMI) transfers the
target's spatial information into a temporal dimension. The traditional focusing method of …

Event-stream representation is the first step for many computer vision tasks using event
cameras. It converts the asynchronous event-streams into a formatted structure so that …

To enhance the accuracy of object detection with event-based neuromorphic vision sensors,
a novel event-based detector named spatiotemporal aggregation transformer (STAT) is …

The swift and accurate detection of traffic signs in traffic scenes is a pivotal aspect of
environmental perception technology in autonomous driving systems. Traffic signs provide …

Object detection plays a crucial role in various cutting-edge applications, such as
autonomous vehicles and advanced robotics systems, primarily relying on conventional …