使用对比学习和方向梯度直方图增强的无监督图像到图像翻译
Enhanced Unsupervised Image-to-Image Translation Using Contrastive Learning and Histogram of Oriented Gradients
AI总结：
本文提出基于CUT模型并结合HOG特征的增强无监督图像到图像翻译方法，通过最小化HOG特征损失保留语义结构，有效减少伪影幻觉并提升图像质量。
摘要：
图像到图像翻译是计算机视觉的重要领域，专注于将图像从一个视觉域转换到另一个视觉域，同时保留其核心内容和结构。然而，该领域面临两大挑战：首先，两个域的数据通常是不成对的，使得难以有效训练生成对抗网络；其次，现有方法在图像生成过程中往往会产生伪影或幻觉，导致图像质量下降。为了解决这些问题，本文提出了一种基于Contrastive Unpaired Translation (CUT)模型的增强无监督图像到图像翻译方法，并结合了Histogram of Oriented Gradients (HOG)特征。这种新方法通过最小化输入图像和生成图像的HOG特征之间的损失，确保了在无语义标签的情况下保留图像的语义结构。该方法在将GTA5数据集中的合成游戏环境翻译为cityscapes数据集中的真实城市场景上进行了测试，结果显示在减少幻觉和提升图像质量方面有显著改善。
英文摘要：
Image-to-Image Translation is a vital area of computer vision that focuses on transforming images from one visual domain to another while preserving their core content and structure. However, this field faces two major challenges: first, the data from the two domains are often unpaired, making it difficult to train generative adversarial networks effectively; second, existing methods tend to produce artifacts or hallucinations during image generation, leading to a decline in image quality. To address these issues, this paper proposes an enhanced unsupervised image-to-image translation method based on the Contrastive Unpaired Translation (CUT) model, incorporating Histogram of Oriented Gradients (HOG) features. This novel approach ensures the preservation of the semantic structure of images, even without semantic labels, by minimizing the loss between the HOG features of input and generated images. The method was tested on translating synthetic game environments from GTA5 dataset to realistic urban scenes in cityscapes dataset, demonstrating significant improvements in reducing hallucinations and enhancing image quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
