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

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 …

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 …

In this paper we make the first attempt at achieving the cross-modal (ie image-to-events)
adaptation for event-based object recognition without accessing any labeled source image …

Recent years have witnessed remarkable achievements in video-based action recognition.
Apart from traditional frame-based cameras, event cameras are bio-inspired vision sensors …

Event cameras are sensors of great interest for many applications that run in low-resource
and challenging environments. They log sparse illumination changes with high temporal …

Comprehensive capturing of human motions requires both accurate captures of complex
poses and precise localization of the human within scenes. Most of the HPE datasets and …

Human Pose Estimation (HPE) based on RGB images has experienced a rapid
development benefiting from deep learning. However, event-based HPE has not been fully …

The event camera asynchronously produces the event stream with a high temporal
resolution, discarding redundant visual information and bringing new possibilities for moving …

Event cameras are a kind of bio-inspired imaging sensor, which asynchronously collect
sparse event streams with many advantages. In this paper, we focus on building better and …