Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentimes their results can be unrealistic with observable color shifts and textures. We believe that this issue results from the divergence between the probabilistic distribution learned by the model and the distribution of natural images. The delicate conditions gradually enlarge the divergence during each sampling timestep. To address this issue, we introduce a new method that brings the predicted samples to the training data manifold using a pretrained unconditional diffusion model. The unconditional model acts as a regularizer and reduces the divergence introduced by the conditional model at each sampling step. We perform comprehensive experiments to demonstrate the effectiveness of our approach on super-resolution, colorization, turbulence removal, and image-deraining tasks. The improvements obtained by our method suggest that the priors can be incorporated as a general plugin for improving conditional diffusion models.

Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images. In this paper, we propose a video generation method based on diffusion models, where the effects of motion are modeled in an implicit condition manner, i.e. one can sample plausible video motions according to the latent feature of frames. We improve the quality of the generated videos by proposing multiple strategies such as sampling space truncation, robustness penalty, and positional group normalization. Various experiments are conducted on datasets consisting of videos with different resolutions and different number of frames. Results show that the proposed method outperforms the state-of-the-art generative adversarial network-based methods by a significant margin in terms of FVD scores as well as perceptible visual quality.

尽管许多远程成像系统旨在支持扩展视力应用，但由于大气湍流，其操作的自然障碍是退化。大气湍流通过引入模糊和几何变形而导致图像质量的显着降解。近年来，在文献中提出了各种基于深度学习的单图像缓解方法，包括基于CNN的基于CNN和基于GAN的反转方法，这些方法试图消除图像中的失真。但是，其中一些方法很难训练，并且通常无法重建面部特征并产生不切实际的结果，尤其是在高湍流的情况下。降级扩散概率模型（DDPM）最近由于其稳定的训练过程和产生高质量图像的能力而获得了一些吸引力。在本文中，我们提出了第一个基于DDPM的解决方案，用于缓解大气湍流问题。我们还提出了一种快速采样技术，用于减少条件DDPM的推理时间。对合成和现实世界数据进行了广泛的实验，以显示我们模型的重要性。为了促进进一步的研究，在审查过程之后，所有代码和验证的模型都将公开。

图像恢复（如Denoising）的最终目的是找到嘈杂和清晰图像域之间的确切相关性。但是，以样本到样本的方式进行了端到端的denoising学习，例如像素损失，这忽略了图像的内在相关性，尤其是语义。在本文中，我们介绍了深层语义统计匹配（D2SM）DENOISISN网络。它利用了预审预测的分类网络的语义特征，然后隐含地与语义特征空间上清晰图像的概率分布匹配。通过学习保留Denocied图像的语义分布，我们从经验上发现我们的方法显着提高了网络的可转换功能，并且可以通过高级视觉任务更好地理解了deNo的结果。在嘈杂的CityScapes数据集上进行的综合实验证明了我们方法对降级性能和语义分割精度的优越性。此外，在我们的扩展任务上观察到的绩效改进，包括超分辨率和飞行实验，表明了其作为新的一般插件组件的潜力。

在许多远程成像的应用中，我们面临的情景，其中出现在捕获的图像中的人通常被大气湍流降级。然而，由于劣化使图像成为几何扭曲和模糊，因此恢复用于面部验证的这种降级的图像是困难的。为了减轻湍流效果，本文提出了第一种湍流缓解方法，该方法利用培训的GaN封装的视觉前沿。基于视觉前沿，我们建议学习在空间周期性上下文距离上保留恢复图像的身份。在考虑网络学习中的身份差异时，这种距离可以保持来自GaN的恢复图像的现实主义。另外，提出了通过在没有身份变化的情况下引入更多外观方差来促进身份保留学习的分层伪连接。广泛的实验表明，我们的方法在恢复结果的视觉质量和面部验证准确性中显着优于现有技术。

基于深度神经网络（DNN）的超分辨率算法大大提高了所生成的图像的质量。然而，由于学习错位光学变焦的困难，这些算法通常会在处理现实世界超分辨率问题时产生重要的伪像。在本文中，我们介绍了一个平方可变形对准网络（SDAN）来解决这个问题。我们的网络了解卷积内核的平方每点偏移，然后基于偏移来对齐纠正卷积窗口的功能。因此，通过提取的对齐的特征将最小化未对准。与Vanilla可变形卷积网络（DCN）中使用的每点偏移不同，我们提出的平方抵消不仅加速了偏移学习，而且还提高了更少参数的发电质量。此外，我们进一步提出了一种高效的交叉包装注意层来提高学习偏移的准确性。它利用包装和解包操作来扩大偏移学习的接收领域，并增强提取低分辨率图像与参考图像之间的空间连接的能力。综合实验在计算效率和现实细节方面表现出我们对其他最先进的方法的方法。

The Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in early December 2019 and now becoming a pandemic. When COVID-19 patients undergo radiography examination, radiologists can observe the present of radiographic abnormalities from their chest X-ray (CXR) images. In this study, a deep convolutional neural network (CNN) model was proposed to aid radiologists in diagnosing COVID-19 patients. First, this work conducted a comparative study on the performance of modified VGG-16, ResNet-50 and DenseNet-121 to classify CXR images into normal, COVID-19 and viral pneumonia. Then, the impact of image augmentation on the classification results was evaluated. The publicly available COVID-19 Radiography Database was used throughout this study. After comparison, ResNet-50 achieved the highest accuracy with 95.88%. Next, after training ResNet-50 with rotation, translation, horizontal flip, intensity shift and zoom augmented dataset, the accuracy dropped to 80.95%. Furthermore, an ablation study on the effect of image augmentation on the classification results found that the combinations of rotation and intensity shift augmentation methods obtained an accuracy higher than baseline, which is 96.14%. Finally, ResNet-50 with rotation and intensity shift augmentations performed the best and was proposed as the final classification model in this work. These findings demonstrated that the proposed classification model can provide a promising result for COVID-19 diagnosis.

Users' physical safety is an increasing concern as the market for intelligent systems continues to grow, where unconstrained systems may recommend users dangerous actions that can lead to serious injury. Covertly unsafe text, language that contains actionable physical harm, but requires further reasoning to identify such harm, is an area of particular interest, as such texts may arise from everyday scenarios and are challenging to detect as harmful. Qualifying the knowledge required to reason about the safety of various texts and providing human-interpretable rationales can shed light on the risk of systems to specific user groups, helping both stakeholders manage the risks of their systems and policymakers to provide concrete safeguards for consumer safety. We propose FARM, a novel framework that leverages external knowledge for trustworthy rationale generation in the context of safety. In particular, FARM foveates on missing knowledge in specific scenarios, retrieves this knowledge with attribution to trustworthy sources, and uses this to both classify the safety of the original text and generate human-interpretable rationales, combining critically important qualities for sensitive domains such as user safety. Furthermore, FARM obtains state-of-the-art results on the SafeText dataset, improving safety classification accuracy by 5.29 points.

In a high dimensional linear predictive regression where the number of potential predictors can be larger than the sample size, we consider using LASSO, a popular L1-penalized regression method, to estimate the sparse coefficients when many unit root regressors are present. Consistency of LASSO relies on two building blocks: the deviation bound of the cross product of the regressors and the error term, and the restricted eigenvalue of the Gram matrix of the regressors. In our setting where unit root regressors are driven by temporal dependent non-Gaussian innovations, we establish original probabilistic bounds for these two building blocks. The bounds imply that the rates of convergence of LASSO are different from those in the familiar cross sectional case. In practical applications given a mixture of stationary and nonstationary predictors, asymptotic guarantee of LASSO is preserved if all predictors are scale-standardized. In an empirical example of forecasting the unemployment rate with many macroeconomic time series, strong performance is delivered by LASSO when the initial specification is guided by macroeconomic domain expertise.

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}.