Separating an image into meaningful underlying components is a crucial first
step for both editing and understanding images. We present a method capable of
selecting the regions of a photograph exhibiting the same material as an
artist-chosen area. Our proposed approach is robust to shading, specular
highlights, and cast shadows, enabling selection in real images. As we do not
rely on semantic segmentation (different woods or metal should not be selected
together), we formulate the problem as a similarity-based grouping problem
based on a user-provided image location. In particular, we propose to leverage
the unsupervised DINO features coupled with a proposed Cross-Similarity module
and an MLP head to extract material similarities in an image. We train our
model on a new synthetic image dataset, that we release. We show that our
method generalizes well to real-world images. We carefully analyze our model's
behavior on varying material properties and lighting. Additionally, we evaluate
it against a hand-annotated benchmark of 50 real photographs. We further
demonstrate our model on a set of applications, including material editing,
in-video selection, and retrieval of object photographs with similar materials.

本文提出了一种能够分离图像中材质相似区域的方法，并在合成图像数据集中进行了验证，证明了其在实际应用中的有效性，可用于材质编辑、视频中的选择和检索具有相似材质的物体照片。

Materialistic: 在图像中选择相似的材料

Materialistic: Selecting Similar Materials in Images

A benchmark of saliency models performance with a synthetic image dataset is
provided. Model performance is evaluated through saliency metrics as well as
the influence of model inspiration and consistency with human psychophysics.
SID4VAM is composed of 230 synthetic images, with known salient regions. Images
were generated with 15 distinct types of low-level features (e.g. orientation,
brightness, color, size...) with a target-distractor pop-out type of synthetic
patterns. We have used Free-Viewing and Visual Search task instructions and 7
feature contrasts for each feature category. Our study reveals that
state-of-the-art Deep Learning saliency models do not perform well with
synthetic pattern images, instead, models with Spectral/Fourier inspiration
outperform others in saliency metrics and are more consistent with human
psychophysical experimentation. This study proposes a new way to evaluate
saliency models in the forthcoming literature, accounting for synthetic images
with uniquely low-level feature contexts, distinct from previous eye tracking
image datasets.

通过使用 230 个合成图像对显著性模型的性能进行了基准测试，研究发现，受到谱 / 傅里叶灵感的模型在显著性测量方面表现更好，并与人类心理物理实验更加一致，提出了一种新的方法来评估显著性模型，考虑了独特的低级特征背景下的合成图像。

SID4VAM: 面向视觉注意力建模的合成图像基准数据集

SID4VAM: A Benchmark Dataset with Synthetic Images for Visual Attention  Modeling

The objective of this paper is to rectify any monocular image by computing a
homography matrix that transforms it to a bird's eye (overhead) view.
We make the following contributions: (i) we show that the homography matrix
can be parameterised with only four parameters that specify the horizon line
and the vertical vanishing point, or only two if the field of view or focal
length is known; (ii) We introduce a novel representation for the geometry of a
line or point (which can be at infinity) that is suitable for regression with a
convolutional neural network (CNN); (iii) We introduce a large synthetic image
dataset with ground truth for the orthogonal vanishing points, that can be used
for training a CNN to predict these geometric entities; and finally (iv) We
achieve state-of-the-art results on horizon detection, with 74.52% AUC on the
Horizon Lines in the Wild dataset. Our method is fast and robust, and can be
used to remove perspective distortion from videos in real time.

本文提出了一种快速、稳健的方法，利用包含地面水平线和垂直消失点等基础几何信息的单张图像进行透视矫正，并且使用合适的神经网络模型进行训练和预测，从而达到了全球最好的水平线检测性能（AUC 值为 74.52%）。该方法可以应用于实时视频透视矫正。