AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Both qualitative and quantitative experimental results demonstrate that CASPNet achieves significant improvements in mitigating pattern repetition and preserving structural integrity, producing high-quality stylized images.
An adaptive feature fusion style transfer model based on the U-Net architecture is proposed, which provides a high-fidelity visual information processing framework with potential methodological implications for intelligent image transmission and multi-scale feature representation in advanced engineering systems.
Structured document understanding requires models to jointly exploit textual, layout, and visual information, but existing discriminative models such as LayoutLMv3 often over-rely on complete OCR text during fine-tuning, limiting their robustness when processing low-quality scans, irregular layouts, or incomplete text....
Style transfer aims to render target content in the style of a reference image, but existing methods often suffer from content leakage, where objects, layouts, or semantics from the style reference appear in the generated output. Although prior data-driven and training-free methods can reduce leakage, they often face a...
With the rapid development of diffusion models for digital image generation, achieving stable control over both semantic composition and visual style remains challenging. Existing approaches often rely on implicit attention or direct style injection, which may cause style–content leakage and unstable semantics. To addr...
The proposed latent diffusion model (LDM)-based framework provides an effective solution for controllable image generation and offers potential value for computational imaging, intelligent visual sensing, and electromagnetic information processing applications requiring high-fidelity semantic representation.
Generative models trained on a single image often struggle to balance global structural integrity and local diversity. Existing single-image generation methods commonly rely on random noise to drive the generation process and lack explicit global structural constraints, making the generated results prone to spatial str...
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