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.
SyncDoc is developed, a synergistic evolution framework built upon LayoutLMv3, which introduces three complementary modules to enhance fine-grained visual structures, a Dynamic Masking Strategy to reduce dependence on textual semantics, and a Deep Cross-modal Orientation module to improve consistency between textual an...
CLeaR, a training-free framework for content-leakage-resistant style transfer, is proposed and experiments demonstrate that CLeaR improves style alignment, reduces content leakage, and achieves better LLM-as-Judge evaluation compared with existing methods.
Palette-Brush Diffusion is proposed, a controllable diffusion framework that decouples style modeling from semantic composition and achieves more consistent style expression and more stable semantic composition than existing methods.
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.
Qualitative and quantitative experiments demonstrate that FRPSS achieves the best Single Image Fr\'echet Inception Distance (SIFID) scores on all three datasets while maintaining competitive Learned Perceptual Image Patch Similarity (LPIPS).
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