The popularity of self-supervised learning has made it possible to train
models without relying on labeled data, which saves expensive annotation costs.
However, most existing self-supervised contrastive learning methods often
overlook the combination of global and local feature information. This paper
proposes a multi-network contrastive learning framework based on global and
local representations. We introduce global and local feature information for
self-supervised contrastive learning through multiple networks. The model
learns feature information at different scales of an image by contrasting the
embedding pairs generated by multiple networks. The framework also expands the
number of samples used for contrast and improves the training efficiency of the
model. Linear evaluation results on three benchmark datasets show that our
method outperforms several existing classical self-supervised learning methods.

提出了基于全局和局部特征表示的多网络对比学习框架，通过多个网络引入自监督对比学习的全局和局部特征信息，并通过对比由多个网络生成的嵌入对来学习图像不同尺度上的特征信息，并扩展对比样本数，提高了模型的训练效率。在三个基准数据集上的线性评估结果表明，我们的方法优于几种现有的经典自监督学习方法。

基于全局和局部表示的多网络对比学习

Multi-network Contrastive Learning Based on Global and Local  Representations

Stable Diffusion model has been extensively employed in the study of
archi-tectural image generation, but there is still an opportunity to enhance
in terms of the controllability of the generated image content. A multi-network
combined text-to-building facade image generating method is proposed in this
work. We first fine-tuned the Stable Diffusion model on the CMP Fa-cades
dataset using the LoRA (Low-Rank Adaptation) approach, then we ap-ply the
ControlNet model to further control the output. Finally, we contrast-ed the
facade generating outcomes under various architectural style text con-tents and
control strategies. The results demonstrate that the LoRA training approach
significantly decreases the possibility of fine-tuning the Stable Dif-fusion
large model, and the addition of the ControlNet model increases the
controllability of the creation of text to building facade images. This
pro-vides a foundation for subsequent studies on the generation of
architectural images.

通过 LoRA 对 CMP Facedes 数据集上的 Stable Diffusion 进行优化，然后应用 ControlNet 模型进一步控制输出，通过对各种建筑风格文本内容和控制策略下的结果进行比较，实验证明 LoRA 训练方法显著降低了对 Stable Diffusion 大模型的微调可能性，ControlNet 模型的添加增强了基于文本生成建筑立面图像的可控性。