Pretrained language models have been shown to encode relational information,
such as the relations between entities or concepts in knowledge-bases --
(Paris, Capital, France). However, simple relations of this type can often be
recovered heuristically and the extent to which models implicitly reflect
topological structure that is grounded in world, such as perceptual structure,
is unknown. To explore this question, we conduct a thorough case study on
color. Namely, we employ a dataset of monolexemic color terms and color chips
represented in CIELAB, a color space with a perceptually meaningful distance
metric.
Using two methods of evaluating the structural alignment of colors in this
space with text-derived color term representations, we find significant
correspondence. Analyzing the differences in alignment across the color
spectrum, we find that warmer colors are, on average, better aligned to the
perceptual color space than cooler ones, suggesting an intriguing connection to
findings from recent work on efficient communication in color naming. Further
analysis suggests that differences in alignment are, in part, mediated by
collocationality and differences in syntactic usage, posing questions as to the
relationship between color perception and usage and context.

本文通过使用预先训练的语言模型，对颜色的结构性关系进行研究，发现在理解色彩的感知结构方面，较为温暖的颜色比较接近于感知颜色空间。

语言模型是否能够在没有基于所见的情况下编码感知结构？以颜色为例的案例研究

Can Language Models Encode Perceptual Structure Without Grounding? A  Case Study in Color

Hairstyle transfer is challenging due to hair structure differences in the
source and target hair. Therefore, we propose Latent Optimization of Hairstyles
via Orthogonalization (LOHO), an optimization-based approach using GAN
inversion to infill missing hair structure details in latent space during
hairstyle transfer. Our approach decomposes hair into three attributes:
perceptual structure, appearance, and style, and includes tailored losses to
model each of these attributes independently. Furthermore, we propose two-stage
optimization and gradient orthogonalization to enable disentangled latent space
optimization of our hair attributes. Using LOHO for latent space manipulation,
users can synthesize novel photorealistic images by manipulating hair
attributes either individually or jointly, transferring the desired attributes
from reference hairstyles. LOHO achieves a superior FID compared with the
current state-of-the-art (SOTA) for hairstyle transfer. Additionally, LOHO
preserves the subject's identity comparably well according to PSNR and SSIM
when compared to SOTA image embedding pipelines. Code is available at
this https URL

本研究提出了一种名为 LOHO 的基于 GAN 逆向的优化方法，通过将头发分解为三个属性：感知结构、外观和风格，并引入定制的损失函数对每个属性进行独立建模，使得在潜空间中填充缺失的头发结构细节，从而实现头发转移。通过 LOHO，用户可以通过单独或联合操作头发属性，从参考发型中传递所需的属性，从而合成新的逼真图像。与现有技术相比，LOHO 在头发转移方面实现了优越的 FID，并在保留主体身份方面表现出与 SOTA 图像嵌入管道相当的性能，提高了头发转移的质量。