This research paper proposes a novel methodology for image-to-image style
transfer on objects utilizing a single deep convolutional neural network. The
proposed approach leverages the You Only Look Once version 8 (YOLOv8)
segmentation model and the backbone neural network of YOLOv8 for style
transfer. The primary objective is to enhance the visual appeal of objects in
images by seamlessly transferring artistic styles while preserving the original
object characteristics. The proposed approach's novelty lies in combining
segmentation and style transfer in a single deep convolutional neural network.
This approach omits the need for multiple stages or models, thus resulting in
simpler training and deployment of the model for practical applications. The
results of this approach are shown on two content images by applying different
style images. The paper also demonstrates the ability to apply style transfer
on multiple objects in the same image.

该研究提出了一种新的图像到图像风格转换方法，利用单个深度卷积神经网络进行对象风格转换。该方法结合了 YOLOv8 分割模型和 YOLOv8 的骨干神经网络，在保留原始对象特征的同时，通过平滑地转换艺术风格来增强图像中的对象的视觉吸引力。该方法将分割和风格转换融合在一个深度卷积神经网络中，避免了多个阶段或模型的需求，从而简化了模型的训练和应用。该论文通过应用不同的风格图像展示了该方法在两个内容图像上的效果，并展示了在同一图像上应用风格转换于多个对象的能力。

基于单个深度网络的升级物体风格迁移

Improved Object-Based Style Transfer with Single Deep Network

Over the past few years, image-to-image style transfer has risen to the
frontiers of neural image processing. While conventional methods were
successful in various tasks such as color and texture transfer between images,
none could effectively work with the custom filter effects that are applied by
users through various platforms like Instagram. In this paper, we introduce a
new concept of style transfer, Filter Style Transfer (FST). Unlike conventional
style transfer, new technique FST can extract and transfer custom filter style
from a filtered style image to a content image. FST first infers the original
image from a filtered reference via image-to-image translation. Then it
estimates filter parameters from the difference between them. To resolve the
ill-posed nature of reconstructing the original image from the reference, we
represent each pixel color of an image to class mean and deviation. Besides, to
handle the intra-class color variation, we propose an uncertainty based
weighted least square method for restoring an original image. To the best of
our knowledge, FST is the first style transfer method that can transfer custom
filter effects between FHD image under 2ms on a mobile device without any
textual context loss.

本论文提出了 Filter Style Transfer (FST) 的新概念，为解决现有传统方法在不同平台上应用的自定义滤镜效果方面的问题，提出了能够在移动设备上以不到 2ms 的速度传输 FHD 图像的 FST 技术。