Deep learning techniques have made considerable progress in image inpainting, restoration, and reconstruction in the last few years. Image outpainting, also known as image extrapolation, lacks attention and practical approaches to be fulfilled, owing to difficulties caused by large-scale area loss and less legitimate neighboring information. These difficulties have made outpainted images handled by most of the existing models unrealistic to human eyes and spatially inconsistent. When upsampling through deconvolution to generate fake content, the naive generation methods may lead to results lacking high-frequency details and structural authenticity. Therefore, as our novelties to handle image outpainting problems, we introduce structural prior as a condition to optimize the generation quality and a new semantic embedding term to enhance perceptual sanity. we propose a deep learning method based on Generative Adversarial Network (GAN) and condition edges as structural prior in order to assist the generation. We use a multi-phase adversarial training scheme that comprises edge inference training, contents inpainting training, and joint training. The newly added semantic embedding loss is proved effective in practice.

In this paper, we propose a novel 3D graph convolution based pipeline for category-level 6D pose and size estimation from monocular RGB-D images. The proposed method leverages an efficient 3D data augmentation and a novel vector-based decoupled rotation representation. Specifically, we first design an orientation-aware autoencoder with 3D graph convolution for latent feature learning. The learned latent feature is insensitive to point shift and size thanks to the shift and scale-invariance properties of the 3D graph convolution. Then, to efficiently decode the rotation information from the latent feature, we design a novel flexible vector-based decomposable rotation representation that employs two decoders to complementarily access the rotation information. The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task. Finally, we propose a 3D deformation mechanism to increase the generalization ability of the pipeline. Extensive experiments show that the proposed pipeline achieves state-of-the-art performance on category-level tasks. Further, the experiments demonstrate that the proposed rotation representation is more suitable for the pose estimation tasks than other rotation representations.

Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource intensive and time-consuming. To tackle this problem, we propose the Fourier-Net, replacing the expansive path in a U-Net style network with a parameter-free model-driven decoder. Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain. This representation is then decoded by our devised model-driven decoder (consisting of a zero padding layer and an inverse discrete Fourier transform layer) to the dense, full-resolution displacement field in the spatial domain. These changes allow our unsupervised Fourier-Net to contain fewer parameters and computational operations, resulting in faster inference speeds. Fourier-Net is then evaluated on two public 3D brain datasets against various state-of-the-art approaches. For example, when compared to a recent transformer-based method, i.e., TransMorph, our Fourier-Net, only using 0.22$\%$ of its parameters and 6.66$\%$ of the mult-adds, achieves a 0.6\% higher Dice score and an 11.48$\times$ faster inference speed. Code is available at \url{https://github.com/xi-jia/Fourier-Net}.

用于机器阅读理解（MRC）的大多数领域适应方法都使用预先训练的问题解答（QA）构造模型来生成用于MRC传输的伪QA对。这样的过程将不可避免地引入不匹配的对（即嘈杂的对应关系），因此由于i）目标文档中不可用的QA对，ii）在将QA构造模型应用于目标域时的域移位。毫无疑问，嘈杂的信件将退化MRC的性能，但是现有作品忽略了MRC的性能。为了解决这样一个未触及的问题，我们建议通过使用与文档相关的对话以及MRC的新域适应方法来构建质量检查对。具体而言，我们建议用于机器阅读理解理解（RMRC）方法的强大域适应性，该方法由答案提取器（AE），问题选择器（QS）和MRC模型组成。具体而言，RMRC通过通过AE估算与文档的相关性来滤除无关的答案，并通过通过QS将候选问题融合在多轮对话聊天中来提取问题。使用提取的QA对，MRC进行了微调，并提供了反馈，以通过一种新颖的增强自我训练方法优化QS。得益于QS的优化，我们的方法将大大减轻域转移引起的嘈杂对应问题。据我们所知，这可能是揭示噪声对应性在域适应MRC模型中的影响的第一个研究，并显示出一种可行的方法来实现与错配对的鲁棒性。在三个数据集上进行的广泛实验证明了我们方法的有效性。

有效的缩放和灵活的任务接口使大型语言模型能够在许多任务中表现出色。帕利（Pali）根据视觉和文本输入生成文本，并使用该界面以许多语言执行许多视觉，语言和多模式任务。为了训练帕利，我们利用了大型的编码器语言模型和视觉变压器（VITS）。这使我们能够利用其现有能力，并利用培训它们的大量成本。我们发现，视觉和语言组成部分的联合缩放很重要。由于现有的语言变压器比其视觉对应物要大得多，因此我们训练迄今为止最大的VIT（VIT-E），以量化甚至大容量视觉模型的好处。为了训练Pali，我们基于一个新的图像文本训练集，其中包含10B图像和文本，以100多种语言来创建大型的多语言组合。帕利（Pali）在多个视觉和语言任务（例如字幕，视觉问题，索方式，场景文本理解）中实现了最新的，同时保留了简单，模块化和可扩展的设计。

由于其极端的长距离建模能力，基于视觉变压器的网络在可变形图像注册中变得越来越流行。但是，我们认为，5层卷积U-NET的接受场足以捕获准确的变形而无需长期依赖性。因此，这项研究的目的是研究与现代变压器的方法相比，将基于U-NET的方法用于医学图像注册时是否已过时。为此，我们通过将平行的卷积块嵌入香草U-NET以增强有效的接受场来提出一个大核U-NET（LKU-NET）。在公共3D IXI Brain Dataset上，用于基于ATLAS的注册，我们表明，香草U-NET的性能已经与基于最新的变压器网络（例如Transmorph）相提并论，并且提出的LKU-NET仅使用其参数的1.12％和其多添加操作的10.8％，优于Transmorph。我们进一步评估了MICCAI Learn2Reg 2021挑战数据集中的LKU-NET，以进行主题间注册，我们的LKU-NET在此数据集中也优于TransMorph，并且在此工作提交后，在公共排行榜上排名第一。只有对香草U-NET的适度修改，我们表明U-NET可以在基于主体间和基于ATLAS的3D医疗图像注册上胜过基于变压器的体系结构。代码可在https://github.com/xi-jia/lku-net上找到。

在图像识别中已广泛提出了生成模型，以生成更多图像，其中分布与真实图像相似。它通常会引入一个歧视网络，以区分真实数据与生成的数据。这样的模型利用了一个歧视网络，该网络负责以区分样式从目标数据集中包含的数据传输的数据。但是，这样做的网络着重于强度分布的差异，并可能忽略数据集之间的结构差异。在本文中，我们制定了一个新的图像到图像翻译问题，以确保生成的图像的结构类似于目标数据集中的图像。我们提出了一个简单但功能强大的结构不稳定的对抗（SUA）网络，该网络在执行图像分割时介绍了训练和测试集之间的强度和结构差异。它由空间变换块组成，然后是强度分布渲染模块。提出了空间变换块来减少两个图像之间的结构缝隙，还产生了一个反变形字段，以使最终的分段图像背部扭曲。然后，强度分布渲染模块将变形结构呈现到具有目标强度分布的图像。实验结果表明，所提出的SUA方法具有在多个数据集之间传递强度分布和结构含量的能力。

人工智能（AI）为简化Covid-19诊断提供了有前景的替代。然而，涉及周围的安全和可信度的担忧阻碍了大规模代表性的医学数据，对临床实践中训练广泛的模型造成了相当大的挑战。为了解决这个问题，我们启动了统一的CT-Covid AI诊断计划（UCADI），其中AI模型可以在没有数据共享的联合学习框架（FL）下在每个主机机构下分发和独立地在没有数据共享的情况下在每个主机机构上执行。在这里，我们认为我们的FL模型通过大的产量（中国测试敏感性/特异性：0.973 / 0.951，英国：0.730 / 0.942），与专业放射科医师的面板实现可比性表现。我们进一步评估了持有的模型（从另外两家医院收集，留出FL）和异构（用造影材料获取）数据，提供了模型所做的决策的视觉解释，并分析了模型之间的权衡联邦培训过程中的性能和沟通成本。我们的研究基于来自位于中国和英国的23家医院的3,336名患者的9,573次胸部计算断层扫描扫描（CTS）。统称，我们的工作提出了利用联邦学习的潜在保留了数字健康的前景。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.