Objective no-reference image- and video-quality metrics are crucial in many
computer vision tasks. However, state-of-the-art no-reference metrics have
become learning-based and are vulnerable to adversarial attacks. The
vulnerability of quality metrics imposes restrictions on using such metrics in
quality control systems and comparing objective algorithms. Also, using
vulnerable metrics as a loss for deep learning model training can mislead
training to worsen visual quality. Because of that, quality metrics testing for
vulnerability is a task of current interest. This paper proposes a new method
for testing quality metrics vulnerability in the physical space. To our
knowledge, quality metrics were not previously tested for vulnerability to this
attack; they were only tested in the pixel space. We applied a physical
adversarial Ti-Patch (Tiled Patch) attack to quality metrics and did
experiments both in pixel and physical space. We also performed experiments on
the implementation of physical adversarial wallpaper. The proposed method can
be used as additional quality metrics in vulnerability evaluation,
complementing traditional subjective comparison and vulnerability tests in the
pixel space. We made our code and adversarial videos available on GitHub:
this https URL

本文提出了一种在物理空间中测试质量度量弱点的方法，该方法可用作评估质量度量韧性的额外指标，补充传统的主观比较和像素空间的弱点测试。

Ti-Patch：用于无参考视频质量度量的平铺物理对抗贴片

Ti-Patch: Tiled Physical Adversarial Patch for no-reference video  quality metrics

We address the problem of learning representations from observations of a
scene involving an agent and an external object the agent interacts with. To
this end, we propose a representation learning framework extracting the
location in physical space of both the agent and the object from unstructured
observations of arbitrary nature. Our framework relies on the actions performed
by the agent as the only source of supervision, while assuming that the object
is displaced by the agent via unknown dynamics. We provide a theoretical
foundation and formally prove that an ideal learner is guaranteed to infer an
isometric representation, disentangling the agent from the object and correctly
extracting their locations. We evaluate empirically our framework on a variety
of scenarios, showing that it outperforms vision-based approaches such as a
state-of-the-art keypoint extractor. We moreover demonstrate how the extracted
representations enable the agent to solve downstream tasks via reinforcement
learning in an efficient manner.

从不受限制的各种观测中提取代理和外部物体在物理空间中的位置的表示学习框架，仅依靠代理执行的动作作为唯一的监督信号，并假设物体通过未知动力学被代理移动，能够确保从中推断出等度量表示，并正确提取它们的位置。

通过互动学习物体的几何表示

Learning Geometric Representations of Objects via Interaction

Embodiment has recently enjoyed renewed consideration as a means to amplify
the faculties of smart machines. Proponents of embodiment seem to imply that
optimizing for movement in physical space promotes something more than the
acquisition of niche capabilities for solving problems in physical space.
However, there is nothing in principle which should so distinguish the problem
of action selection in physical space from the problem of action selection in
more abstract spaces, like that of language. Rather, what makes embodiment
persuasive as a means toward higher intelligence is that it promises to
capture, but does not actually realize, contingent facts about certain bodies
(living intelligence) and the patterns of activity associated with them. These
include an active resistance to annihilation and revisable constraints on the
processes that make the world intelligible. To be theoretically or practically
useful beyond the creation of niche tools, we argue that "embodiment" cannot be
the trivial fact of a body, nor its movement through space, but the perpetual
negotiation of the function, design, and integrity of that
body$\unicode{x2013}$that is, to participate in what it means to
$\textit{constitute}$ a given body. It follows that computer programs which are
strictly incapable of traversing physical space might, under the right
conditions, be more embodied than a walking, talking robot.

本文认为，融合智能体的行动选择问题和更抽象空间中的行动选择问题，并通过体现某些具体身体的情况和活动模式，采用更加严谨和实用的方式解决问题，而这并不特指身体运动或者遍历物理空间，同时论述了计算机程序在何种条件下，能比行走、说话机器人更加体现感知