Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so far. A document textline usually contains words that are spatially and semantically correlated, which can be easily obtained from OCR engines. In this paper, we propose Wukong-Reader, trained with new pre-training objectives to leverage the structural knowledge nested in document textlines. We introduce textline-region contrastive learning to achieve fine-grained alignment between the visual regions and texts of document textlines. Furthermore, masked region modeling and textline-grid matching are also designed to enhance the visual and layout representations of textlines. Experiments show that our Wukong-Reader has superior performance on various VDU tasks such as information extraction. The fine-grained alignment over textlines also empowers Wukong-Reader with promising localization ability.

潜在空间基于能量的模型（EBM），也称为基于能量的先验，引起了对生成建模的日益兴趣。由于其在潜在空间的配方和强大的建模能力方面的灵活性所推动，最近构建的作品已经进行了有趣的尝试，目的是针对文本建模的解释性。但是，潜在空间EBM还继承了数据空间中EBM的一些缺陷。实践中退化的MCMC抽样质量会导致培训中的发电质量和不稳定差，尤其是在具有复杂潜在结构的数据上。受到最近的努力的启发，该努力利用扩散恢复的可能性学习是解决抽样问题的一种方法，我们在变异学习框架中引入了扩散模型和潜在空间EBM之间的新型共生，这是潜在扩散能量基于能量的模型。我们与信息瓶颈共同开发基于几何聚类的正则化，以进一步提高学到的潜在空间的质量。对几个具有挑战性的任务进行的实验证明了我们模型在可解释的文本建模上的优越性能而不是强大的同行。

智力是通过连接主义或典型主义者实现的吗？虽然连接主义方法取得了超人的性能，但已经越来越多的证据表明，这些特定的特定优势在系统泛化中特别脆弱。这种观察表明了连接主义和典型主义者之间的中央辩论，其中后者不断地倡导认知架构中的代数治疗。在这项工作中，我们遵循典型主义者的呼叫，并提出一种混合方法来提高推理系统的泛化。具体而言，我们展示了具有代数表示的原型，用于乌鸦的渐进矩阵（RPM）的抽象空间 - 时间推理任务，并呈现代数感知神经半符号（Alans）学习者。艾拉斯学习者受到抽象代数和代表理论的动机。它由神经视觉感知前端和代数抽象推理后端组成：前端总结了基于对象的表示的可视信息，而后端将其转换为代数结构，并在飞行中引导隐藏的操作员。稍后执行诱导的操作员以预测答案的表示，并且选择与预测最相似的选择作为解决方案。广泛的实验表明，通过纳入代数处理，艾拉斯学习者优于需要系统泛化的域中的各种纯粹连接主义模型。我们进一步表明学习的代数表示可以通过同构以产生答案来解码。

我们呈现深度区域竞争（DRC），这是一种旨在以完全无监督的方式从图像中提取前景对象的算法。前景提取可以被视为一种特殊的泛型图像分段的情况，专注于从背景中识别和解开对象。在这项工作中，我们通过以专家（MOE）的混合形式的生成图像建模和生成图像建模来重新思考前景提取，我们进一步介绍了学习的像素重新分配作为捕获规律的基本诱导偏差背景区域。通过这种建模，可以通过期望最大化（EM）自然地发现前景背景分区。我们表明，该方法有效利用了在分区过程中混合成分之间的相互作用，该分区过程紧密地连接到区域竞争，是通用图像分割的一个精细方法。实验表明，与现有方法相比，DRC在复杂的真实数据上表现出更具竞争力的性能和具有挑战性的多对象场景。此外，我们认为，即使在训练期间看不见的类别，DRC也可能概括为新的前景物体。

了解网格单元如何执行路径集成计算仍然是一个根本的问题。在本文中，我们对网格单元进行了对路径集成的一般表示模型的理论分析，其中2D自身位被编码为更高的尺寸向量，并且通过向量的一般转换表示2D自动。我们确定转型的两个条件。一个是路径集成所必需的组表示条件。另一个是一种各向同性的缩放条件，可确保局部共形地嵌入，使得向量表示中的误差符合在2D自身位置中的误差。然后，我们调查最简单的转换，即线性变换，将其显式代数和几何结构揭示为矩阵旋转，并探索各向同性缩放条件与特殊类六角网格图案之间的连接。最后，通过基于优化的方法，我们可以学习六边形网格模式，该网格图案在啮齿动物大脑中共享网格细胞的相似性质。学习模型能够准确地长距离路径集成。代码可在https://github.com/ruiqigao/grid-cell-path中获得。

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.

Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency. We believe the insufficient utilization of training signals should be responsible. To alleviate this issue, we introduce a conceptually simple yet learning-efficient MIM training scheme, termed Disjoint Masking with Joint Distillation (DMJD). For disjoint masking (DM), we sequentially sample multiple masked views per image in a mini-batch with the disjoint regulation to raise the usage of tokens for reconstruction in each image while keeping the masking rate of each view. For joint distillation (JD), we adopt a dual branch architecture to respectively predict invisible (masked) and visible (unmasked) tokens with superior learning targets. Rooting in orthogonal perspectives for training efficiency improvement, DM and JD cooperatively accelerate the training convergence yet not sacrificing the model generalization ability. Concretely, DM can train ViT with half of the effective training epochs (3.7 times less time-consuming) to report competitive performance. With JD, our DMJD clearly improves the linear probing classification accuracy over ConvMAE by 5.8%. On fine-grained downstream tasks like semantic segmentation, object detection, etc., our DMJD also presents superior generalization compared with state-of-the-art SSL methods. The code and model will be made public at https://github.com/mx-mark/DMJD.

Considering the computation complexity, we propose a Guided Hybrid Quantization with One-to-one Self-Teaching (GHOST}) framework. More concretely, we first design a structure called guided quantization self-distillation (GQSD), which is an innovative idea for realizing lightweight through the synergy of quantization and distillation. The training process of the quantization model is guided by its full-precision model, which is time-saving and cost-saving without preparing a huge pre-trained model in advance. Second, we put forward a hybrid quantization (HQ) module to obtain the optimal bit width automatically under a constrained condition where a threshold for distribution distance between the center and samples is applied in the weight value search space. Third, in order to improve information transformation, we propose a one-to-one self-teaching (OST) module to give the student network a ability of self-judgment. A switch control machine (SCM) builds a bridge between the student network and teacher network in the same location to help the teacher to reduce wrong guidance and impart vital knowledge to the student. This distillation method allows a model to learn from itself and gain substantial improvement without any additional supervision. Extensive experiments on a multimodal dataset (VEDAI) and single-modality datasets (DOTA, NWPU, and DIOR) show that object detection based on GHOST outperforms the existing detectors. The tiny parameters (<9.7 MB) and Bit-Operations (BOPs) (<2158 G) compared with any remote sensing-based, lightweight or distillation-based algorithms demonstrate the superiority in the lightweight design domain. Our code and model will be released at https://github.com/icey-zhang/GHOST.

Automatic font generation without human experts is a practical and significant problem, especially for some languages that consist of a large number of characters. Existing methods for font generation are often in supervised learning. They require a large number of paired data, which are labor-intensive and expensive to collect. In contrast, common unsupervised image-to-image translation methods are not applicable to font generation, as they often define style as the set of textures and colors. In this work, we propose a robust deformable generative network for unsupervised font generation (abbreviated as DGFont++). We introduce a feature deformation skip connection (FDSC) to learn local patterns and geometric transformations between fonts. The FDSC predicts pairs of displacement maps and employs the predicted maps to apply deformable convolution to the low-level content feature maps. The outputs of FDSC are fed into a mixer to generate final results. Moreover, we introduce contrastive self-supervised learning to learn a robust style representation for fonts by understanding the similarity and dissimilarities of fonts. To distinguish different styles, we train our model with a multi-task discriminator, which ensures that each style can be discriminated independently. In addition to adversarial loss, another two reconstruction losses are adopted to constrain the domain-invariant characteristics between generated images and content images. Taking advantage of FDSC and the adopted loss functions, our model is able to maintain spatial information and generates high-quality character images in an unsupervised manner. Experiments demonstrate that our model is able to generate character images of higher quality than state-of-the-art methods.