In the text-to-image generation field, recent remarkable progress in Stable
Diffusion makes it possible to generate rich kinds of novel photorealistic
images. However, current models still face misalignment issues (e.g.,
problematic spatial relation understanding and numeration failure) in complex
natural scenes, which impedes the high-faithfulness text-to-image generation.
Although recent efforts have been made to improve controllability by giving
fine-grained guidance (e.g., sketch and scribbles), this issue has not been
fundamentally tackled since users have to provide such guidance information
manually. In this work, we strive to synthesize high-fidelity images that are
semantically aligned with a given textual prompt without any guidance. Toward
this end, we propose a coarse-to-fine paradigm to achieve layout planning and
image generation. Concretely, we first generate the coarse-grained layout
conditioned on a given textual prompt via in-context learning based on Large
Language Models. Afterward, we propose a fine-grained object-interaction
diffusion method to synthesize high-faithfulness images conditioned on the
prompt and the automatically generated layout. Extensive experiments
demonstrate that our proposed method outperforms the state-of-the-art models in
terms of layout and image generation. Our code and settings are available at
https://layoutllm-t2i.github.io.

本研究提出了一种从文本提示中合成与之语义对齐的高保真度图像的方法，采用了粗到细的范式来实现布局规划和图像生成，并通过粒度不同的目标交互扩散方法以优于现有方法的方式生成布局和图像。

LayoutLLM-T2I: 从 LLM 中引导布局为文本到图像生成

LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image  Generation

We present a novel formulation to removing reflection from polarized images
in the wild. We first identify the misalignment issues of existing reflection
removal datasets where the collected reflection-free images are not perfectly
aligned with input mixed images due to glass refraction. Then we build a new
dataset with more than 100 types of glass in which obtained transmission images
are perfectly aligned with input mixed images. Second, capitalizing on the
special relationship between reflection and polarized light, we propose a
polarized reflection removal model with a two-stage architecture. In addition,
we design a novel perceptual NCC loss that can improve the performance of
reflection removal and general image decomposition tasks. We conduct extensive
experiments, and results suggest that our model outperforms state-of-the-art
methods on reflection removal.

提出一种新颖的方法，使用极化图像来消除反射，该方法解决了现有反射去除数据集中由于玻璃折射导致反射区域与无反射区域的不完全对齐问题。该方法还采用了双阶段架构的极化反射消除模型和一种新的感知 NCC 损失，可以在反射消除和图像分解任务方面提高性能，并在大量的实验中证明其胜过现有的反射消除方法。