In individuals with sensory-motor impairments, missing limb functions can be restored using
neuroprosthetic devices that directly interface with the nervous system. However, restoring …

We introduce N-ImageNet, a large-scale dataset targeted for robust, fine-grained object
recognition with event cameras. The dataset is collected using programmable hardware in …

Recognizing objects from sparse and noisy events becomes extremely difficult when paired
images and category labels do not exist. In this paper, we study label-free event-based …

We introduce Ev-TTA, a simple, effective test-time adaptation algorithm for event-based
object recognition. While event cameras are proposed to provide measurements of scenes …

Recent advances in 2D zero-shot and few-shot recognition often leverage large pre-trained
vision-language models (VLMs) such as CLIP. Due to a shortage of suitable datasets, it is …

Unsupervised feature extraction algorithms form one of the most important building blocks in
machine learning systems. These algorithms are often adapted to the event-based domain …

Benefited from the high temporal resolution and high dynamic range, spike cameras have
shown great potential in recognizing high-speed moving objects. However, the computer …

This study presents a framework for detecting mechanical damage in pipelines, focusing on
generating simulated data and sampling to emulate distributed acoustic sensing (DAS) …

Dynamic vision sensors (DVSs) are emerging neuromorphic visual capturing devices, with
great advantages in terms of low-power consumption, wide dynamic range, and high …

Neuromorphic engineering aims to build (autonomous) systems by mimicking biological
systems. It is motivated by the observation that biological organisms—from algae to primates …