Scene Text Editing (STE) is a challenging research problem, and it aims to
modify existing texts in an image while preserving the background and the font
style of the original text of the image. Due to its various real-life
applications, researchers have explored several approaches toward STE in recent
years. However, most of the existing STE methods show inferior editing
performance because of (1) complex image backgrounds, (2) various font styles,
and (3) varying word lengths within the text. To address such inferior editing
performance issues, in this paper, we propose a novel font-agnostic scene text
editing framework, named FAST, for simultaneously generating text in arbitrary
styles and locations while preserving a natural and realistic appearance
through combined mask generation and style transfer. The proposed approach
differs from the existing methods as they directly modify all image pixels.
Instead, the proposed method has introduced a filtering mechanism to remove
background distractions, allowing the network to focus solely on the text
regions where editing is required. Additionally, a text-style transfer module
has been designed to mitigate the challenges posed by varying word lengths.
Extensive experiments and ablations have been conducted, and the results
demonstrate that the proposed method outperforms the existing methods both
qualitatively and quantitatively.

本文提出了一种名为 FAST 的新颖无字体场景文本编辑框架，用于在保留自然和逼真外观的同时，同时在任意样式和位置生成文本，通过结合遮罩生成和样式转移。通过滤波机制去除背景干扰，使网络仅集中于需要编辑的文本区域，同时设计了文本样式转移模块以减轻不同字长度带来的挑战，实验证明该方法在质量和数量上优于现有方法。

FAST：面向字体无关的场景文本编辑

FAST: Font-Agnostic Scene Text Editing

Semi-supervised learning is a challenging problem which aims to construct a
model by learning from a limited number of labeled examples. Numerous methods
have been proposed to tackle this problem, with most focusing on utilizing the
predictions of unlabeled instances consistency alone to regularize networks.
However, treating labeled and unlabeled data separately often leads to the
discarding of mass prior knowledge learned from the labeled examples, and
failure to mine the feature interaction between the labeled and unlabeled image
pairs. In this paper, we propose a novel method for semi-supervised semantic
segmentation named GuidedMix-Net, by leveraging labeled information to guide
the learning of unlabeled instances. Specifically, we first introduce a feature
alignment objective between labeled and unlabeled data to capture potentially
similar image pairs and then generate mixed inputs from them. The proposed
mutual information transfer (MITrans), based on the cluster assumption, is
shown to be a powerful knowledge module for further progressive refining
features of unlabeled data in the mixed data space. To take advantage of the
labeled examples and guide unlabeled data learning, we further propose a mask
generation module to generate high-quality pseudo masks for the unlabeled data.
Along with supervised learning for labeled data, the prediction of unlabeled
data is jointly learned with the generated pseudo masks from the mixed data.
Extensive experiments on PASCAL VOC 2012, PASCAL-Context and Cityscapes
demonstrate the effectiveness of our GuidedMix-Net, which achieves competitive
segmentation accuracy and significantly improves the mIoU by +7$\%$ compared to
previous state-of-the-art approaches.

本文提出了一种基于混合数据的指导式半监督分割方法 GuidedMix-Net，并利用标记数据指导未标记数据的学习，该方法采用特征对齐和互信息传输的方法进行特征融合，同时提出伪标签生成模块用于生成高质量伪标签，联合训练标记数据和伪标签，实验验证了该方法在各种数据集中取得了具有竞争力的分割精度和显着改进的 mIoU。

GuidedMix-Net：使用标记的图像作为参考学习改进伪掩码

GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as  Reference

This paper presents a weakly-supervised approach to object instance
segmentation. Starting with known or predicted object bounding boxes, we learn
object masks by playing a game of cut-and-paste in an adversarial learning
setup. A mask generator takes a detection box and Faster R-CNN features, and
constructs a segmentation mask that is used to cut-and-paste the object into a
new image location. The discriminator tries to distinguish between real
objects, and those cut and pasted via the generator, giving a learning signal
that leads to improved object masks. We verify our method experimentally using
Cityscapes, COCO, and aerial image datasets, learning to segment objects
without ever having seen a mask in training. Our method exceeds the performance
of existing weakly supervised methods, without requiring hand-tuned segment
proposals, and reaches 90% of supervised performance.

本文提出了一种基于弱监督学习框架的对象实例分割方法，使用对抗训练的方法学习对象掩模，利用剪切 - 粘贴的游戏过程，通过掩模生成器和区分器的协同作用来实现分割掩模的优化，实验结果表明，该方法在不需要手工标注分割建议的情况下，可以超越现有的弱监督分割方法，并获得了 90% 的监督分割性能。