Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. However, due to the complicated background textures and various text styles, existing methods fall short in generating clear and legible edited text images. In this study, we attribute the poor editing performance to two problems: 1) Implicit decoupling structure. Previous methods of editing the whole image have to learn different translation rules of background and text regions simultaneously. 2) Domain gap. Due to the lack of edited real scene text images, the network can only be well trained on synthetic pairs and performs poorly on real-world images. To handle the above problems, we propose a novel network by MOdifying Scene Text image at strokE Level (MOSTEL). Firstly, we generate stroke guidance maps to explicitly indicate regions to be edited. Different from the implicit one by directly modifying all the pixels at image level, such explicit instructions filter out the distractions from background and guide the network to focus on editing rules of text regions. Secondly, we propose a Semi-supervised Hybrid Learning to train the network with both labeled synthetic images and unpaired real scene text images. Thus, the STE model is adapted to real-world datasets distributions. Moreover, two new datasets (Tamper-Syn2k and Tamper-Scene) are proposed to fill the blank of public evaluation datasets. Extensive experiments demonstrate that our MOSTEL outperforms previous methods both qualitatively and quantitatively. Datasets and code will be available at https://github.com/qqqyd/MOSTEL.

Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for challenging recognition rather than pure visual classification. However, how to effectively model the linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet++ for scene text spotting. Firstly, the autonomous suggests enforcing explicitly language modeling by decoupling the recognizer into vision model and language model and blocking gradient flow between both models. Secondly, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation. Thirdly, we propose an execution manner of iterative correction for the language model which can effectively alleviate the impact of noise input. Finally, to polish ABINet++ in long text recognition, we propose to aggregate horizontal features by embedding Transformer units inside a U-Net, and design a position and content attention module which integrates character order and content to attend to character features precisely. ABINet++ achieves state-of-the-art performance on both scene text recognition and scene text spotting benchmarks, which consistently demonstrates the superiority of our method in various environments especially on low-quality images. Besides, extensive experiments including in English and Chinese also prove that, a text spotter that incorporates our language modeling method can significantly improve its performance both in accuracy and speed compared with commonly used attention-based recognizers.

Data-efficient learning on graphs (GEL) is essential in real-world applications. Existing GEL methods focus on learning useful representations for nodes, edges, or entire graphs with ``small'' labeled data. But the problem of data-efficient learning for subgraph prediction has not been explored. The challenges of this problem lie in the following aspects: 1) It is crucial for subgraphs to learn positional features to acquire structural information in the base graph in which they exist. Although the existing subgraph neural network method is capable of learning disentangled position encodings, the overall computational complexity is very high. 2) Prevailing graph augmentation methods for GEL, including rule-based, sample-based, adaptive, and automated methods, are not suitable for augmenting subgraphs because a subgraph contains fewer nodes but richer information such as position, neighbor, and structure. Subgraph augmentation is more susceptible to undesirable perturbations. 3) Only a small number of nodes in the base graph are contained in subgraphs, which leads to a potential ``bias'' problem that the subgraph representation learning is dominated by these ``hot'' nodes. By contrast, the remaining nodes fail to be fully learned, which reduces the generalization ability of subgraph representation learning. In this paper, we aim to address the challenges above and propose a Position-Aware Data-Efficient Learning framework for subgraph neural networks called PADEL. Specifically, we propose a novel node position encoding method that is anchor-free, and design a new generative subgraph augmentation method based on a diffused variational subgraph autoencoder, and we propose exploratory and exploitable views for subgraph contrastive learning. Extensive experiment results on three real-world datasets show the superiority of our proposed method over state-of-the-art baselines.

There still remains an extreme performance gap between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) when training from scratch on small datasets, which is concluded to the lack of inductive bias. In this paper, we further consider this problem and point out two weaknesses of ViTs in inductive biases, that is, the spatial relevance and diverse channel representation. First, on spatial aspect, objects are locally compact and relevant, thus fine-grained feature needs to be extracted from a token and its neighbors. While the lack of data hinders ViTs to attend the spatial relevance. Second, on channel aspect, representation exhibits diversity on different channels. But the scarce data can not enable ViTs to learn strong enough representation for accurate recognition. To this end, we propose Dynamic Hybrid Vision Transformer (DHVT) as the solution to enhance the two inductive biases. On spatial aspect, we adopt a hybrid structure, in which convolution is integrated into patch embedding and multi-layer perceptron module, forcing the model to capture the token features as well as their neighboring features. On channel aspect, we introduce a dynamic feature aggregation module in MLP and a brand new "head token" design in multi-head self-attention module to help re-calibrate channel representation and make different channel group representation interacts with each other. The fusion of weak channel representation forms a strong enough representation for classification. With this design, we successfully eliminate the performance gap between CNNs and ViTs, and our DHVT achieves a series of state-of-the-art performance with a lightweight model, 85.68% on CIFAR-100 with 22.8M parameters, 82.3% on ImageNet-1K with 24.0M parameters. Code is available at https://github.com/ArieSeirack/DHVT.

布局生成是计算机视觉中的一项新任务，它结合了对象本地化和美学评估中的挑战，在广告，海报和幻灯片设计中广泛使用。准确而愉快的布局应考虑布局元素内的内域关系以及布局元素与图像之间的域间关系。但是，大多数以前的方法只是专注于图像 - 范围 - 不平衡的布局生成，而无需利用图像中复杂的视觉信息。为此，我们探索了一个名为“图像条件的布局生成”的新颖范式，该范式旨在以语义连贯的方式将文本叠加层添加到图像中。具体而言，我们提出了一个图像条件的变分变压器（ICVT），该变形变压器（ICVT）在图像中生成各种布局。首先，采用自我注意的机制来对布局元素内的上下文关系进行建模，而交叉注意机制用于融合条件图像的视觉信息。随后，我们将它们作为有条件变异自动编码器（CVAE）的构件，表现出吸引人的多样性。其次，为了减轻布局元素域和视觉域之间的差距，我们设计了一个几何对齐模块，其中图像的几何信息与布局表示形式对齐。此外，我们构建了一个大规模的广告海报布局设计数据集，并具有精致的布局和显着图。实验结果表明，我们的模型可以在图像的非侵入区域中自适应生成布局，从而产生和谐的布局设计。

人类视频运动转移（HVMT）的目的是鉴于源头的形象，生成了模仿驾驶人员运动的视频。 HVMT的现有方法主要利用生成对抗网络（GAN），以根据根据源人员图像和每个驾驶视频框架估计的流量来执行翘曲操作。但是，由于源头，量表和驾驶人员之间的巨大差异，这些方法始终会产生明显的人工制品。为了克服这些挑战，本文提出了基于gan的新型人类运动转移（远程移动）框架。为了产生逼真的动作，远遥采用了渐进的一代范式：它首先在没有基于流动的翘曲的情况下生成每个身体的零件，然后将所有零件变成驾驶运动的完整人。此外，为了保留自然的全球外观，我们设计了一个全球对齐模块，以根据其布局与驾驶员的规模和位置保持一致。此外，我们提出了一个纹理对准模块，以使人的每个部分都根据纹理的相似性对齐。最后，通过广泛的定量和定性实验，我们的远及以两个公共基准取得了最先进的结果。

利用TRIMAP引导和融合多级功能是具有像素级预测的基于Trimap的垫子的两个重要问题。为了利用Trimap指导，大多数现有方法只需将TRIMAPS和图像连接在一起，以馈送深网络或应用额外的网络以提取更多的TRIMAP指导，这符合效率和效率之间的冲突。对于新兴的基于内容的特征融合，大多数现有的消光方法仅关注本地特征，这些功能缺乏与有趣对象相关的强大语义信息的全局功能的指导。在本文中，我们提出了一种由我们的Trimap引导的非背景多尺度池（TMP）模块和全球本地背景信息融合（GLF）模块组成的Trimap-Goided Feats挖掘和融合网络。考虑到Trimap提供强大的语义指导，我们的TMP模块在Trimap的指导下对有趣的对象进行了有效的特征挖掘，而无需额外参数。此外，我们的GLF模块使用我们的TMP模块开采的有趣物体的全局语义信息，以指导有效的全局本地上下文感知多级功能融合。此外，我们建立了一个共同的有趣的物体消光（CIOM）数据集，以推进高质量的图像消光。在组合物-1K测试集，Alphamatting基准和我们的CIOM测试集上的实验结果表明，我们的方法优于最先进的方法。代码和模型将很快公开发布。

基于关注的编码器 - 解码器框架在现场文本识别中变得流行，主要是由于其在从视觉和语义域集成识别线索方面的优越性。然而，最近的研究表明，这两个线索可能在困难的文本中错位（例如，具有稀有文本形状）并引入诸如角色位置的约束来缓解问题。尽管有一定的成功，但无内容的位置嵌入稳定地与有意义的本地图像区域嵌入。在本文中，我们提出了一种名为多域字符距离感知（MDCDP）的新型模块，以建立视觉和语义相关位置编码。 MDCDP使用位置嵌入在注意机制后查询视觉和语义功能。它自然地编码了位置线索，其描述了字符之间的视觉和语义距离。我们开发一个名为CDISTNET的新型架构，堆叠MDCDP几次以指导精确的距离建模。因此，即使呈现的各种困难，视觉语义对准也很好地建造。我们将CDISTNET应用于两个增强的数据集和六个公共基准。实验表明，CDISTNET实现了最先进的识别准确性。虽然可视化也表明CDISTNET在视觉和语义域中实现了适当的注意本地化。我们将在验收时发布我们的代码。

Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.

A step-search sequential quadratic programming method is proposed for solving nonlinear equality constrained stochastic optimization problems. It is assumed that constraint function values and derivatives are available, but only stochastic approximations of the objective function and its associated derivatives can be computed via inexact probabilistic zeroth- and first-order oracles. Under reasonable assumptions, a high-probability bound on the iteration complexity of the algorithm to approximate first-order stationarity is derived. Numerical results on standard nonlinear optimization test problems illustrate the advantages and limitations of our proposed method.