几十年来，手写的中文文本识别（HCTR）一直是一个活跃的研究主题。但是，大多数以前的研究仅关注裁剪文本图像的识别，而忽略了实际应用程序中文本线检测引起的错误。尽管近年来已经提出了一些针对页面文本识别的方法，但它们要么仅限于简单布局，要么需要非常详细的注释，包括昂贵的线条级别甚至角色级边界框。为此，我们建议Pagenet端到端弱监督的页面级HCTR。 Pagenet检测并识别角色并预测其之间的阅读顺序，在处理复杂的布局（包括多方向和弯曲的文本线路）时，这更健壮和灵活。利用所提出的弱监督学习框架，Pagenet只需要对真实数据进行注释。但是，它仍然可以在字符和线级别上输出检测和识别结果，从而避免标记字符和文本线条的界限框的劳动和成本。在五个数据集上进行的广泛实验证明了Pagenet优于现有的弱监督和完全监督的页面级方法。这些实验结果可能会引发进一步的研究，而不是基于连接主义时间分类或注意力的现有方法的领域。源代码可在https://github.com/shannanyinxiang/pagenet上获得。

在线和离线手写的中文文本识别（HTCR）已经研究了数十年。早期方法采用了基于过度裂段的策略，但遭受低速，准确性不足和角色分割注释的高成本。最近，基于连接主义者时间分类（CTC）和注意机制的无分割方法主导了HCTR的领域。但是，人们实际上是按字符读取文本的，尤其是对于中文等意识形态图。这就提出了一个问题：无细分策略真的是HCTR的最佳解决方案吗？为了探索此问题，我们提出了一种基于细分的新方法，用于识别使用简单但有效的完全卷积网络实现的手写中文文本。提出了一种新型的弱监督学习方法，以使网络仅使用笔录注释进行训练。因此，可以避免以前基于细分的方法所需的昂贵字符分割注释。由于缺乏完全卷积网络中的上下文建模，我们提出了一种上下文正则化方法，以在培训阶段将上下文信息集成到网络中，这可以进一步改善识别性能。在四个广泛使用的基准测试中进行的广泛实验，即Casia-HWDB，Casia-Olhwdb，ICDAR2013和Scut-HCCDOC，表明我们的方法在线和离线HCTR上都显着超过了现有方法，并且表现出比CTC/ CTC/ CTC/ CTC/ CTC/速度高得多的方法。基于注意力的方法。

摄像头捕获的文档图像通常会遭受透视和几何变形的影响。在考虑视觉不良美学和OCR系统性能不断恶化时，纠正它们是很大的价值。最近的基于学习的方法将重点放在精确的文档图像上。但是，这可能不足以克服实际挑战，包括具有大边缘区域或没有边缘的文档图像。由于这种不切实际，用户在遇到大边缘区域时努力进行裁剪。同时，没有边距的脱瓦图像仍然是一个无法克服的问题。据我们所知，仍然没有完整有效的管道来纠正野外文档图像。为了解决这个问题，我们提出了一种称为Marior的新方法（删除边缘和\迭代内容纠正）。马里奥（Marior）遵循一种渐进策略，以粗到精细的方式迭代地改善脱水质量和可读性。具体而言，我们将管道分为两个模块：边缘去除模块（MRM）和迭代内容整流模块（ICRM）。首先，我们预测输入图像的分割面膜以删除边缘，从而获得初步结果。然后，我们通过产生密集的位移流以实现内容感知的整流来进一步完善图像。我们可以适应地确定改进的迭代次数。实验证明了我们方法在公共基准测试方面的最先进性能。资源可在https://github.com/zzzhang-jx/marior上获得，以进行进一步比较。

由于其在隐私保护，文档修复和文本编辑方面的各种应用，因此删除文本引起了越来越多的关注。它显示出深度神经网络的重大进展。但是，大多数现有方法通常会为复杂的背景产生不一致的结果。为了解决此问题，我们提出了一个上下文引导的文本删除网络，称为CTRNET。 Ctrnet探索了低级结构和高级判别上下文特征，作为指导背景恢复过程的先验知识。我们进一步提出了具有CNNS和Transformer-编码器的局部全球含量建模（LGCM）块，以捕获局部特征并在全球像素之间建立长期关系。最后，我们将LGCM与特征建模和解码的上下文指南合并。在基准数据集，Scut-Enstext和Scut-Syn上进行的实验表明，CTRNET显着胜过现有的最新方法。此外，关于考试论文的定性实验也证明了我们方法的概括能力。代码和补充材料可在https://github.com/lcy0604/ctrnet上获得。

事实证明，多模式文档预训练的模型在各种视觉上富裕的文档理解（VRDU）任务中非常有效。尽管现有的文档预先培训模型在VRDU的标准基准上取得了出色的性能，但它们建模和利用文档上的视觉和语言之间的互动的方式阻碍了他们无法获得更好的概括能力和更高的准确性。在这项工作中，我们主要从监督信号的角度研究了VRDU视觉联合表示学习的问题。具体而言，提出了一种称为BI-VLDOC的预训练范式，其中设计了双向视觉监督策略和视觉性混合注意机制，以完全探索并利用这两种方式之间的相互作用，以学习更强的交叉交叉方式 - 具有更丰富语义的模式文档表示。 Bi-Vldoc受益于学习丰富的跨模式文档表示形式，显着提高了三个广泛使用文档的最新性能，理解基准，包括形式的理解（从85.14％到93.44％），收据信息提取（从96.01％到97.84％）和文档分类（从96.08％到97.12％）。在文档视觉质量检查中，BI-VLDOC与以前的单个模型方法相比，实现了最先进的性能。

几乎所有场景文本发现（检测和识别）方法依赖于昂贵的框注释（例如，文本线框，单词级框和字符级框）。我们首次证明培训场景文本发现模型可以通过每个实例的单点的极低成本注释来实现。我们提出了一种端到端的场景文本发现方法，将场景文本拍摄作为序列预测任务，如语言建模。给予图像作为输入，我们将所需的检测和识别结果作为一系列离散令牌制定，并使用自动回归变压器来预测序列。我们在几个水平，多面向和任意形状的场景文本基准上实现了有希望的结果。最重要的是，我们表明性能对点注释的位置不是很敏感，这意味着它可以比需要精确位置的边界盒更容易地注释并自动生成。我们认为，这种先锋尝试表明了场景文本的重要机会，比以前可能的比例更大的比例更大。

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .