Recently, event cameras have shown large applicability in several computer
vision fields especially concerning tasks that require high temporal
resolution. In this work, we investigate the usage of such kind of data for
emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based
Facial Expression Recognition. NEFER is composed of paired RGB and event videos
representing human faces labeled with the respective emotions and also
annotated with face bounding boxes and facial landmarks. We detail the data
acquisition process as well as providing a baseline method for RGB and event
data. The collected data captures subtle micro-expressions, which are hard to
spot with RGB data, yet emerge in the event domain. We report a double
recognition accuracy for the event-based approach, proving the effectiveness of
a neuromorphic approach for analyzing fast and hardly detectable expressions
and the emotions they conceal.

本文介绍了新型事件相机对于人脸情绪识别的应用以及介绍一个 NEFER 数据集，经过实验证明，采用 NEFER 数据集和神经形态学的方法可以更有效地分析人脸微小而难以察觉的表情和它们所隐藏的情感。

神经形态事件驱动的面部表情识别

Neuromorphic Event-based Facial Expression Recognition

The field of artificial intelligence has seen explosive growth and
exponential success. The last phase of development showcased deep learnings
ability to solve a variety of difficult problems across a multitude of domains.
Many of these networks met and exceeded human benchmarks by becoming experts in
the domains in which they are trained. Though the successes of artificial
intelligence have begun to overshadow its failures, there is still much that
separates current artificial intelligence tools from becoming the exceptional
general learners that humans are. In this paper, we identify the ten
commandments upon which human intelligence is systematically and hierarchically
built. We believe these commandments work collectively to serve as the
essential ingredients that lead to the emergence of higher-order cognition and
intelligence. This paper discusses a computational framework that could house
these ten commandments and suggests new architectural modifications that could
lead to the development of smarter, more explainable, and generalizable
artificial systems inspired by a neuromorphic approach.

本文旨在构建一个计算框架，基于仿神经模型的方法，实现分层的人类智能十诫，为发展更加智能、可解释、通用的人工智能系统提供指引。

人工智能和人工通用智能之间的桥梁：一种十诫框架来获得人类智能

Bridging the Gap between Artificial Intelligence and Artificial General Intelligence: A Ten Commandment Framework for Human-Like Intelligence

Depth estimation is an important computer vision task, useful in particular
for navigation in autonomous vehicles, or for object manipulation in robotics.
Here we solved it using an end-to-end neuromorphic approach, combining two
event-based cameras and a Spiking Neural Network (SNN) with a slightly modified
U-Net-like encoder-decoder architecture, that we named StereoSpike. More
specifically, we used the Multi Vehicle Stereo Event Camera Dataset (MVSEC). It
provides a depth ground-truth, which was used to train StereoSpike in a
supervised manner, using surrogate gradient descent. We propose a novel readout
paradigm to obtain a dense analog prediction -- the depth of each pixel -- from
the spikes of the decoder. We demonstrate that this architecture generalizes
very well, even better than its non-spiking counterparts, leading to
state-of-the-art test accuracy. To the best of our knowledge, it is the first
time that such a large-scale regression problem is solved by a fully spiking
network. Finally, we show that low firing rates (<10%) can be obtained via
regularization, with a minimal cost in accuracy. This means that StereoSpike
could be efficiently implemented on neuromorphic chips, opening the door for
low power and real time embedded systems.

使用神经形态学方法和多车立体事件相机数据集 (MVSEC)，结合双脉冲事件相机和脉冲神经网络 (SNN) 的编码器 - 解码器架构，开发了一种新颖的神经元模仿方法 (StereoSpike) 来解决深度估计问题，并得到了最先进的测试准确性。