A de facto standard in solving computer vision problems is to use a common
high-resolution camera and choose its placement on an agent (i.e., position and
orientation) based on human intuition. On the other hand, extremely simple and
well-designed visual sensors found throughout nature allow many organisms to
perform diverse, complex behaviors. In this work, motivated by these examples,
we raise the following questions: 1. How effective simple visual sensors are in
solving vision tasks? 2. What role does their design play in their
effectiveness? We explore simple sensors with resolutions as low as one-by-one
pixel, representing a single photoreceptor First, we demonstrate that just a
few photoreceptors can be enough to solve many tasks, such as visual navigation
and continuous control, reasonably well, with performance comparable to that of
a high-resolution camera. Second, we show that the design of these simple
visual sensors plays a crucial role in their ability to provide useful
information and successfully solve these tasks. To find a well-performing
design, we present a computational design optimization algorithm and evaluate
its effectiveness across different tasks and domains, showing promising
results. Finally, we perform a human survey to evaluate the effectiveness of
intuitive designs devised manually by humans, showing that the computationally
found design is among the best designs in most cases.

通过探索简单的视觉传感器在解决视觉任务中的有效性和设计的作用，我们展示了少数光感受器即可解决诸多任务，并通过计算设计优化算法找到了具有良好性能的设计。

用简单感光元件而非相机解决视觉任务

Solving Vision Tasks with Simple Photoreceptors Instead of Cameras

Visual sensors are not only becoming better at capturing high-quality images
but also they have steadily increased their capabilities in processing data on
their own on-chip. Yet the majority of VO pipelines rely on the transmission
and processing of full images in a centralized unit (e.g. CPU or GPU), which
often contain much redundant and low-quality information for the task. In this
paper, we address the task of frame-to-frame rotational estimation but, instead
of reasoning about relative motion between frames using the full images,
distribute the estimation at pixel-level. In this paradigm, each pixel produces
an estimate of the global motion by only relying on local information and local
message-passing with neighbouring pixels. The resulting per-pixel estimates can
then be communicated to downstream tasks, yielding higher-level, informative
cues instead of the original raw pixel-readings. We evaluate the proposed
approach on real public datasets, where we offer detailed insights about this
novel technique and open-source our implementation for the future benefit of
the community.

本文提出一种新的方法，通过像素层面的局部信息和邻域像素的本地信息传递，实现对帧间旋转估计的估算，从而产生高级、有信息的线索，而不是原始像素读数。

PixRO: 基于像素分布的旋转测距与高斯置信传播

PixRO: Pixel-Distributed Rotational Odometry with Gaussian Belief  Propagation

Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and
conventional frame cameras, are increasingly integrated into edge-side
intelligent machines. Realizing intensive multi-sensory data analysis directly
on edge intelligent machines is crucial for numerous emerging edge
applications, such as augmented and virtual reality and unmanned aerial
vehicles, which necessitates unified data representation, unprecedented
hardware energy efficiency and rapid model training. However, multi-sensory
data are intrinsically heterogeneous, causing significant complexity in the
system development for edge-side intelligent machines. In addition, the
performance of conventional digital hardware is limited by the physically
separated processing and memory units, known as the von Neumann bottleneck, and
the physical limit of transistor scaling, which contributes to the slowdown of
Moore's law. These limitations are further intensified by the tedious training
of models with ever-increasing sizes. We propose a novel hardware-software
co-design, random resistive memory-based deep extreme point learning machine
(DEPLM), that offers efficient unified point set analysis. We show the system's
versatility across various data modalities and two different learning tasks.
Compared to a conventional digital hardware-based system, our co-design system
achieves huge energy efficiency improvements and training cost reduction when
compared to conventional systems. Our random resistive memory-based deep
extreme point learning machine may pave the way for energy-efficient and
training-friendly edge AI across various data modalities and tasks.

通过硬件软件的协同设计，我们提出了基于随机电阻存储器的深度极端点学习机，实现了高效统一的点集分析，比传统系统节省了大量能源并降低了训练成本，为各种数据模态和任务提供了节能高效的边缘人工智能解决方案。

基于随机电阻存储的深度极值学习机的统一视觉处理

Random resistive memory-based deep extreme point learning machine for  unified visual processing

Robots that interact with humans in a physical space or application need to
think about the person's posture, which typically comes from visual sensors
like cameras and infra-red. Artificial intelligence and machine learning
algorithms use information from these sensors either directly or after some
level of symbolic abstraction, and the latter usually partitions the range of
observed values to discretize the continuous signal data. Although these
representations have been effective in a variety of algorithms with respect to
accuracy and task completion, the underlying models are rarely interpretable,
which also makes their outputs more difficult to explain to people who request
them. Instead of focusing on the possible sensor values that are familiar to a
machine, we introduce a qualitative spatial reasoning approach that describes
the human posture in terms that are more familiar to people. This paper
explores the derivation of our symbolic representation at two levels of detail
and its preliminary use as features for interpretable activity recognition.

介绍了一种基于人类肢体空间关系推理的符号表示方法，用于解释机器人看到的人体姿态，实现可解释的活动识别。