Visual prompting infuses visual information into the input image to adapt
models toward specific predictions and tasks. Recently, manually crafted
markers such as red circles are shown to guide the model to attend to a target
region on the image. However, these markers only work on models trained with
data containing those markers. Moreover, finding these prompts requires
guesswork or prior knowledge of the domain on which the model is trained. This
work circumvents manual design constraints by proposing to learn the visual
prompts for guiding the attention of vision transformers. The learned visual
prompt, added to any input image would redirect the attention of the
pre-trained vision transformer to its spatial location on the image.
Specifically, the prompt is learned in a self-supervised manner without
requiring annotations and without fine-tuning the vision transformer. Our
experiments demonstrate the effectiveness of the proposed optimization-based
visual prompting strategy across various pre-trained vision encoders.

通过在输入图像中引入视觉提示信息，本研究旨在为视觉变换器模型设计学习视觉提示，以引导其注意力集中在图像的特定区域，通过自监督学习的方式进行优化，实验结果表明该优化策略在各种预训练视觉编码器中的效果显著。

引导视觉转换器的视觉提示学习

Learning Visual Prompts for Guiding the Attention of Vision Transformers

Reconstructing and tracking deformable surface with little or no texture has
posed long-standing challenges. Fundamentally, the challenges stem from
textureless surfaces lacking features for establishing cross-image
correspondences. In this work, we present a novel type of markers to
proactively enrich the object's surface features, and thereby ease the 3D
surface reconstruction and correspondence tracking. Our markers are made of
fluorescent dyes, visible only under the ultraviolet (UV) light and invisible
under regular lighting condition. Leveraging the markers, we design a
multi-camera system that captures surface deformation under the UV light and
the visible light in a time multiplexing fashion. Under the UV light, markers
on the object emerge to enrich its surface texture, allowing high-quality 3D
shape reconstruction and tracking. Under the visible light, markers become
invisible, allowing us to capture the object's original untouched appearance.
We perform experiments on various challenging scenes, including hand gestures,
facial expressions, waving cloth, and hand-object interaction. In all these
cases, we demonstrate that our system is able to produce robust, high-quality
3D reconstruction and tracking.

利用荧光染料制作的标记物为表面特征丰富提供了一种新方法，从而简化了三维表面重建和对应跟踪的过程，并展示了在不同场景下的稳健高质量的三维重建和跟踪能力。

无纹理可变形表面重建与隐形标记

Textureless Deformable Surface Reconstruction with Invisible Markers

Machine-learned systems are in widespread use for making decisions about
humans, and it is important that they are fair, i.e., not biased against
individuals based on sensitive attributes. We present runtime verification of
algorithmic fairness for systems whose models are unknown, but are assumed to
have a Markov chain structure. We introduce a specification language that can
model many common algorithmic fairness properties, such as demographic parity,
equal opportunity, and social burden. We build monitors that observe a long
sequence of events as generated by a given system, and output, after each
observation, a quantitative estimate of how fair or biased the system was on
that run until that point in time. The estimate is proven to be correct modulo
a variable error bound and a given confidence level, where the error bound gets
tighter as the observed sequence gets longer. Our monitors are of two types,
and use, respectively, frequentist and Bayesian statistical inference
techniques. While the frequentist monitors compute estimates that are
objectively correct with respect to the ground truth, the Bayesian monitors
compute estimates that are correct subject to a given prior belief about the
system's model. Using a prototype implementation, we show how we can monitor if
a bank is fair in giving loans to applicants from different social backgrounds,
and if a college is fair in admitting students while maintaining a reasonable
financial burden on the society. Although they exhibit different theoretical
complexities in certain cases, in our experiments, both frequentist and
Bayesian monitors took less than a millisecond to update their verdicts after
each observation.

本文介绍了一种基于统计推断的系统的运行时监测技术，该系统具有未知的结构但是被假定为具有马尔可夫链结构。我们引入了一种规范语言，可以模拟许多常见的算法公平性属性，例如人口平均，平等机会和社会负担。使用原型实现，我们展示了如何监测银行在不同社会背景的申请人给予贷款以及学院在保持合理的社会负担的情况下公平地录取学生。