Crater mapping using neural networks and other automated methods has
increased recently with automated Crater Detection Algorithms (CDAs) applied to
planetary bodies throughout the solar system. A recent publication by Benedix
et al. (2020) showed high performance at small scales compared to similar
automated CDAs but with a net positive diameter bias in many crater candidates.
I compare the publicly available catalogs from Benedix et al. (2020) and Lee &
Hogan (2021) and show that the reported performance is sensitive to the metrics
used to test the catalogs. I show how the more permissive comparison methods
indicate a higher CDA performance by allowing worse candidate craters to match
ground-truth craters. I show that the Benedix et al. (2020) catalog has a
substantial performance loss with increasing latitude and identify an image
projection issue that might cause this loss. Finally, I suggest future
applications of neural networks in generating large scientific datasets be
validated using secondary networks with independent data sources or training
methods.

用神经网络和其他自动化方法进行撞击坑映射的研究近年来有所增加，应用于太阳系各个行星体的自动化撞击坑检测算法（CDAs）。本文比较了 Benedix 等人（2020）和 Lee＆Hogan（2021）的公开可用目录，并展示了使用测试目录的指标对报告性能敏感。作者通过采用更宽松的比较方法指出，允许较差的候选撞击坑与真实撞击坑匹配可以获得更高的 CDA 性能。作者展示了 Benedix 等人（2020）的目录在不断增加的纬度下有较大的性能损失，并确定了可能导致此损失的图像投影问题。最后，作者建议使用独立数据源或训练方法的辅助网络对生成的大型科学数据集中神经网络的应用进行验证。

比较 Benedix 等人 (2020) 和 Lee 和 Hogan (2021) 的自动撞击坑目录

Comparison of automated crater catalogs for Mars from Benedix et al.  (2020) and Lee and Hogan (2021)

As an important and challenging problem in computer vision, PAnoramic
Semantic Segmentation (PASS) gives complete scene perception based on an
ultra-wide angle of view. Usually, prevalent PASS methods with 2D panoramic
image input focus on solving image distortions but lack consideration of the 3D
properties of original $360^{\circ}$ data. Therefore, their performance will
drop a lot when inputting panoramic images with the 3D disturbance. To be more
robust to 3D disturbance, we propose our Spherical Geometry-Aware Transformer
for PAnoramic Semantic Segmentation (SGAT4PASS), considering 3D spherical
geometry knowledge. Specifically, a spherical geometry-aware framework is
proposed for PASS. It includes three modules, i.e., spherical geometry-aware
image projection, spherical deformable patch embedding, and a panorama-aware
loss, which takes input images with 3D disturbance into account, adds a
spherical geometry-aware constraint on the existing deformable patch embedding,
and indicates the pixel density of original $360^{\circ}$ data, respectively.
Experimental results on Stanford2D3D Panoramic datasets show that SGAT4PASS
significantly improves performance and robustness, with approximately a 2%
increase in mIoU, and when small 3D disturbances occur in the data, the
stability of our performance is improved by an order of magnitude. Our code and
supplementary material are available at
this https URL

提出了一种考虑三维球面几何知识的全景语义分割方法，包括球面几何感知的图像投影、球形变形块嵌入和全景感知损失，实验结果表明该方法在稳定性和性能上均有显著提升。

SGAT4PASS: 球面几何感知变换器用于全景语义分割

SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic  Segmentation

Recently, a surge of advanced facial editing techniques have been proposed
that leverage the generative power of a pre-trained StyleGAN. To successfully
edit an image this way, one must first project (or invert) the image into the
pre-trained generator's domain. As it turns out, however, StyleGAN's latent
space induces an inherent tradeoff between distortion and editability, i.e.
between maintaining the original appearance and convincingly altering some of
its attributes. Practically, this means it is still challenging to apply
ID-preserving facial latent-space editing to faces which are out of the
generator's domain. In this paper, we present an approach to bridge this gap.
Our technique slightly alters the generator, so that an out-of-domain image is
faithfully mapped into an in-domain latent code. The key idea is pivotal tuning
- a brief training process that preserves the editing quality of an in-domain
latent region, while changing its portrayed identity and appearance. In Pivotal
Tuning Inversion (PTI), an initial inverted latent code serves as a pivot,
around which the generator is fined-tuned. At the same time, a regularization
term keeps nearby identities intact, to locally contain the effect. This
surgical training process ends up altering appearance features that represent
mostly identity, without affecting editing capabilities. We validate our
technique through inversion and editing metrics, and show preferable scores to
state-of-the-art methods. We further qualitatively demonstrate our technique by
applying advanced edits (such as pose, age, or expression) to numerous images
of well-known and recognizable identities. Finally, we demonstrate resilience
to harder cases, including heavy make-up, elaborate hairstyles and/or headwear,
which otherwise could not have been successfully inverted and edited by
state-of-the-art methods.

本文提出了一种基于 Pivotal Tuning Inversion (PTI) 的人脸编辑方法，通过微调生成器来实现将不在生成器领域内的图像还原为在域内的潜在代码，从而提高人脸编辑的效果和质量。

基于潜在变量编辑真实图像的关键调整

Pivotal Tuning for Latent-based Editing of Real Images

Generative adversarial networks (GANs) learn to synthesise new samples from a
high-dimensional distribution by passing samples drawn from a latent space
through a generative network. When the high-dimensional distribution describes
images of a particular data set, the network should learn to generate visually
similar image samples for latent variables that are close to each other in the
latent space. For tasks such as image retrieval and image classification, it
may be useful to exploit the arrangement of the latent space by projecting
images into it, and using this as a representation for discriminative tasks.
GANs often consist of multiple layers of non-linear computations, making them
very difficult to invert. This paper introduces techniques for projecting image
samples into the latent space using any pre-trained GAN, provided that the
computational graph is available. We evaluate these techniques on both MNIST
digits and Omniglot handwritten characters. In the case of MNIST digits, we
show that projections into the latent space maintain information about the
style and the identity of the digit. In the case of Omniglot characters, we
show that even characters from alphabets that have not been seen during
training may be projected well into the latent space; this suggests that this
approach may have applications in one-shot learning.

通过先前训练好的 GAN 模型，将图像投影到潜在空间中以获取表示，我们可以在 MNIST 任务和 Omniglot 任务中成功地保留数字和字符的信息，这种方法可以应用于一次性学习。