Drone deployment has become crucial in a variety of applications, including solutions to
traffic issues in metropolitan areas and highways. On the other hand, data collected via …

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 …

Retrieving accurate semantic information in challenging high dynamic range (HDR) and
high-speed conditions remains an open challenge for image-based algorithms due to …

DAVIS camera, streaming two complementary sensing modalities of asynchronous events
and frames, has gradually been used to address major object detection challenges (eg, fast …

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 …

Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while
increasing resource consumption (energy, bandwidth, and computation). Neuromorphic …

This paper proposes a pre-trained neural network for handling event camera data. Our
model is a self-supervised learning framework, and uses paired event camera data and …

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 …

Moving Object Detection (MOD) is a critical vision task for successfully achieving safe
autonomous driving. Despite plausible results of deep learning methods, most existing …

In the past years, attention-based Transformers have swept across the field of computer
vision, starting a new stage of backbones in semantic segmentation. Nevertheless, semantic …