WSSL：面向图像修复的加权自监督学习框架
WSSL: Weighted Self-supervised Learning Framework For Image-inpainting
AI总结：
针对现有监督式图像修复方法泛化性差、全局上下文捕捉不足的问题，提出加权自监督学习框架WSSL，通过多加权前置任务学习特征，并结合重建与感知损失的新型损失函数，实现更优的图像修复效果。
摘要：
图像修复是对图像中缺失部分进行重建的过程。基于监督算法的方法虽已取得出色效果，但存在两个显著缺陷：在未见过的数据上测试时表现不佳，且无法捕捉图像的全局上下文，导致生成结果视觉效果差。为解决这些问题，我们提出一种新型图像修复自监督学习框架——加权自监督学习（Weighted Self-Supervised Learning，WSSL）。我们设计WSSL从多个加权前置任务中学习特征，再将这些特征用于下游任务即图像修复。为提升框架性能并生成视觉效果更优的图像，我们还提出了一种新型图像修复损失函数，该函数结合重建损失和感知损失来重建图像。实验表明，WSSL优于现有方法，我们提出的损失函数有助于取得更好的效果。
英文摘要：
Image inpainting is the process of regenerating lost parts of the image. Supervised algorithm-based methods have shown excellent results but have two significant drawbacks. They do not perform well when tested with unseen data. They fail to capture the global context of the image, resulting in a visually unappealing result. We propose a novel self-supervised learning framework for image-inpainting: Weighted Self-Supervised Learning (WSSL) to tackle these problems. We designed WSSL to learn features from multiple weighted pretext tasks. These features are then utilized for the downstream task, image-inpainting. To improve the performance of our framework and produce more visually appealing images, we also present a novel loss function for image inpainting. The loss function takes advantage of both reconstruction loss and perceptual loss functions to regenerate the image. Our experimentation shows WSSL outperforms previous methods, and our loss function helps produce better results.
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。
