This paper addresses the problem of cross-modal object tracking from RGB videos and
event data. Rather than constructing a complex cross-modal fusion network, we explore the …

Multi-view spectral clustering has become appealing due to its good performance in
capturing the correlations among all views. However, on one hand, many existing methods …

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 …

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 …

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

High-quality and challenging event stream datasets play an important role in the design of
an efficient event-driven mechanism that mimics the brain. Although event cameras can …

Event cameras are ideally suited to capture High Dynamic Range (HDR) visual information
without blur but provide poor imaging capability for static or slowly varying scenes …

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

Event cameras, or dynamic vision sensors, have recently achieved success from
fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously …