Dynamic magnetic resonance image reconstruction from incomplete k-space data has generated great research interest due to its capability to reduce scan time. Never-theless, the reconstruction problem is still challenging due to its ill-posed nature. Recently, diffusion models espe-cially score-based generative models have exhibited great potential in algorithm robustness and usage flexi-bility. Moreover, the unified framework through the variance exploding stochastic differential equation (VE-SDE) is proposed to enable new sampling methods and further extend the capabilities of score-based gener-ative models. Therefore, by taking advantage of the uni-fied framework, we proposed a k-space and image Du-al-Domain collaborative Universal Generative Model (DD-UGM) which combines the score-based prior with low-rank regularization penalty to reconstruct highly under-sampled measurements. More precisely, we extract prior components from both image and k-space domains via a universal generative model and adaptively handle these prior components for faster processing while maintaining good generation quality. Experimental comparisons demonstrated the noise reduction and detail preservation abilities of the proposed method. Much more than that, DD-UGM can reconstruct data of differ-ent frames by only training a single frame image, which reflects the flexibility of the proposed model.

Although recent deep learning methods, especially generative models, have shown good performance in fast magnetic resonance imaging, there is still much room for improvement in high-dimensional generation. Considering that internal dimensions in score-based generative models have a critical impact on estimating the gradient of the data distribution, we present a new idea, low-rank tensor assisted k-space generative model (LR-KGM), for parallel imaging reconstruction. This means that we transform original prior information into high-dimensional prior information for learning. More specifically, the multi-channel data is constructed into a large Hankel matrix and the matrix is subsequently folded into tensor for prior learning. In the testing phase, the low-rank rotation strategy is utilized to impose low-rank constraints on tensor output of the generative network. Furthermore, we alternately use traditional generative iterations and low-rank high-dimensional tensor iterations for reconstruction. Experimental comparisons with the state-of-the-arts demonstrated that the proposed LR-KGM method achieved better performance.

Low-dose computed tomography (CT) plays a significant role in reducing the radiation risk in clinical applications. However, lowering the radiation dose will significantly degrade the image quality. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT imaging algorithms. Therefore, we propose a fully unsupervised one sample diffusion model (OSDM)in projection domain for low-dose CT reconstruction. To extract sufficient prior information from single sample, the Hankel matrix formulation is employed. Besides, the penalized weighted least-squares and total variation are introduced to achieve superior image quality. Specifically, we first train a score-based generative model on one sinogram by extracting a great number of tensors from the structural-Hankel matrix as the network input to capture prior distribution. Then, at the inference stage, the stochastic differential equation solver and data consistency step are performed iteratively to obtain the sinogram data. Finally, the final image is obtained through the filtered back-projection algorithm. The reconstructed results are approaching to the normal-dose counterparts. The results prove that OSDM is practical and effective model for reducing the artifacts and preserving the image quality.

磁共振成像是临床诊断的重要工具。但是，它遭受了漫长的收购时间。深度学习的利用，尤其是深层生成模型，在磁共振成像中提供了积极的加速和更好的重建。然而，学习数据分布作为先验知识并从有限数据中重建图像仍然具有挑战性。在这项工作中，我们提出了一种新颖的Hankel-K空间生成模型（HKGM），该模型可以从一个k-空间数据的训练集中生成样品。在先前的学习阶段，我们首先从k空间数据构建一个大的Hankel矩阵，然后从大型Hankel矩阵中提取多个结构化的K空间贴片，以捕获不同斑块之间的内部分布。从Hankel矩阵中提取斑块使生成模型可以从冗余和低级别的数据空间中学习。在迭代重建阶段，可以观察到所需的解决方案遵守学识渊博的先验知识。通过将其作为生成模型的输入来更新中间重建解决方案。然后，通过对测量数据对其Hankel矩阵和数据一致性组合施加低排名的惩罚来替代地进行操作。实验结果证实，单个K空间数据中斑块的内部统计数据具有足够的信息来学习强大的生成模型并提供最新的重建。

近年来，基于深度学习的平行成像（PI）取得了巨大进展，以加速磁共振成像（MRI）。然而，现有方法的性能和鲁棒性仍然可以是不受欢迎的。在这项工作中，我们建议通过柔性PI重建，创建的重量K-Space Genera-Tive模型（WKGM）来探索K空间域学习。具体而言，WKGM是一种通用的K空间域模型，在其中有效地纳入了K空间加权技术和高维空间增强设计，用于基于得分的Genererative模型训练，从而实现良好和强大的重建。此外，WKGM具有灵活性，因此可以与各种传统的K空间PI模型协同结合，从而产生基于学习的先验以产生高保真重建。在具有不同采样模式和交流电因子的数据集上进行实验性重新构建表明，WKGM可以通过先验良好的K-Space生成剂获得最新的重建结果。

减少磁共振（MR）图像采集时间可能会使MR检查更容易获得。包括深度学习模型在内的先前艺术已致力于解决长期MRI成像时间的问题。最近，深层生成模型在算法鲁棒性和使用灵活性方面具有巨大的潜力。然而，无法直接学习或使用任何现有方案。此外，还值得研究的是，深层生成模型如何在混合域上很好地工作。在这项工作中，通过利用基于深度能量的模型，我们提出了一个K空间和图像域协作生成模型，以全面估算从采样量未采样的测量中的MR数据。与最先进的实验比较表明，所提出的混合方法的重建精度较小，在不同的加速因子下更稳定。

无监督的深度学习最近证明了生产高质量样本的希望。尽管它具有促进图像着色任务的巨大潜力，但由于数据歧管和模型能力的高维度，性能受到限制。这项研究提出了一种新的方案，该方案利用小波域中的基于得分的生成模型来解决这些问题。通过利用通过小波变换来利用多尺度和多渠道表示，该模型可以共同有效地从堆叠的粗糙小波系数组件中了解较富裕的先验。该策略还降低了原始歧管的维度，并减轻了维度的诅咒，这对估计和采样有益。此外，设计了小波域中的双重一致性项，即数据一致性和结构一致性，以更好地利用着色任务。具体而言，在训练阶段，一组由小波系数组成的多通道张量被用作训练网络以denoising得分匹配的输入。在推论阶段，样品是通过具有数据和结构一致性的退火Langevin动力学迭代生成的。实验证明了所提出的方法在发电和着色质量方面的显着改善，尤其是在着色鲁棒性和多样性方面。

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.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.