Hyperbolic geometry, a Riemannian manifold endowed with constant sectional
negative curvature, has been considered an alternative embedding space in many
learning scenarios, \eg, natural language processing, graph learning, \etc, as
a result of its intriguing property of encoding the data's hierarchical
structure (like irregular graph or tree-likeness data). Recent studies prove
that such data hierarchy also exists in the visual dataset, and investigate the
successful practice of hyperbolic geometry in the computer vision (CV) regime,
ranging from the classical image classification to advanced model adaptation
learning. This paper presents the first and most up-to-date literature review
of hyperbolic spaces for CV applications. To this end, we first introduce the
background of hyperbolic geometry, followed by a comprehensive investigation of
algorithms, with geometric prior of hyperbolic space, in the context of visual
applications. We also conclude this manuscript and identify possible future
directions.

本文是第一篇也是目前最全面的关于超几何空间在计算机视觉应用领域的文献综述，讨论了超几何空间背景、算法及其在图像分类等视觉数据处理中的应用，并对未来研究方向进行了探讨。

计算机视觉中的双曲几何：一项调查

Hyperbolic Geometry in Computer Vision: A Survey

Machine learning models are known to perpetuate and even amplify the biases
present in the data. However, these data biases frequently do not become
apparent until after the models are deployed. Our work tackles this issue and
enables the preemptive analysis of large-scale datasets. REVISE (REvealing
VIsual biaSEs) is a tool that assists in the investigation of a visual dataset,
surfacing potential biases along three dimensions: (1) object-based, (2)
person-based, and (3) geography-based. Object-based biases relate to the size,
context, or diversity of the depicted objects. Person-based metrics focus on
analyzing the portrayal of people within the dataset. Geography-based analyses
consider the representation of different geographic locations. These three
dimensions are deeply intertwined in how they interact to bias a dataset, and
REVISE sheds light on this; the responsibility then lies with the user to
consider the cultural and historical context, and to determine which of the
revealed biases may be problematic. The tool further assists the user by
suggesting actionable steps that may be taken to mitigate the revealed biases.
Overall, the key aim of our work is to tackle the machine learning bias problem
early in the pipeline. REVISE is available at
this https URL

本研究旨在解决机器学习模型中存在的数据偏见问题。我们开发了 REVISE 工具，可针对视觉数据集展开预防性分析。该工具可以从物体、人物和地理等三个层面揭示潜在的偏见，并对分析结果提出可操作的建议。

REVISE: 衡量和减轻视觉数据集中偏差的工具

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

Technological developments call for increasing perception and action
capabilities of robots. Among other skills, vision systems that can adapt to
any possible change in the working conditions are needed. Since these
conditions are unpredictable, we need benchmarks which allow to assess the
generalization and robustness capabilities of our visual recognition
algorithms. In this work we focus on robotic kitting in unconstrained
scenarios. As a first contribution, we present a new visual dataset for the
kitting task. Differently from standard object recognition datasets, we provide
images of the same objects acquired under various conditions where camera,
illumination and background are changed. This novel dataset allows for testing
the robustness of robot visual recognition algorithms to a series of different
domain shifts both in isolation and unified. Our second contribution is a novel
online adaptation algorithm for deep models, based on batch-normalization
layers, which allows to continuously adapt a model to the current working
conditions. Differently from standard domain adaptation algorithms, it does not
require any image from the target domain at training time. We benchmark the
performance of the algorithm on the proposed dataset, showing its capability to
fill the gap between the performances of a standard architecture and its
counterpart adapted offline to the given target domain.

本文提出了一种针对机器人操作中暴露的变化性的视觉基准测试方法，它包括一个新的针对机器人拼装任务的数据集，一个在线适应算法，以及对这些算法的基准测试，这将有助于评估机器人视觉识别算法的泛化能力与鲁棒性。