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 which an event camera consistently moves around
a monitor displaying images from ImageNet. N-ImageNet serves as a challenging
benchmark for event-based object recognition, due to its large number of
classes and samples. We empirically show that pretraining on N-ImageNet
improves the performance of event-based classifiers and helps them learn with
few labeled data. In addition, we present several variants of N-ImageNet to
test the robustness of event-based classifiers under diverse camera
trajectories and severe lighting conditions, and propose a novel event
representation to alleviate the performance degradation. To the best of our
knowledge, we are the first to quantitatively investigate the consequences
caused by various environmental conditions on event-based object recognition
algorithms. N-ImageNet and its variants are expected to guide practical
implementations for deploying event-based object recognition algorithms in the
real world.

本研究介绍了 N-ImageNet 数据集，用于强健细粒度物体识别与事件摄像头。该数据集经过程序控制的硬件收集，具有大量的类别和样本，已被证实在事件物体识别的预训练中，有助于学习少量标注数据，同时还提出了 N-ImageNet 的几个变体以测试事件分类器在各种环境下的稳健性。本研究也提出了一种新的事件表示法，以缓解性能降级，并在环境条件不同的情况下比较事件物体识别算法之间的差异。因此，N-ImageNet 及其变体有望指导实际部署事件物体识别算法在现实世界中的使用。