条件分布是描述响应与预测因子之间关系的基本数量。我们提出了一种学习条件分布的Wasserstein生成方法。所提出的方法使用条件发生器将已知分布转换为目标条件分布。通过匹配涉及条件发生器和目标关节分布的联合分布估计条件发生器，使用Wassersein距离作为这些关节分布的差异测量。我们建立了所提出的方法产生的条件采样分布的非渐近误差，并表明它能够减轻维度的诅咒，假设数据分布被支持在低维集上。我们进行数值实验以验证提出的方法，并将其应用于条件采样生成，非参数条件密度估计，预测不确定性量化，二抗体响应数据，图像重构和图像生成的应用。

Information Extraction (IE) aims to extract structured information from heterogeneous sources. IE from natural language texts include sub-tasks such as Named Entity Recognition (NER), Relation Extraction (RE), and Event Extraction (EE). Most IE systems require comprehensive understandings of sentence structure, implied semantics, and domain knowledge to perform well; thus, IE tasks always need adequate external resources and annotations. However, it takes time and effort to obtain more human annotations. Low-Resource Information Extraction (LRIE) strives to use unsupervised data, reducing the required resources and human annotation. In practice, existing systems either utilize self-training schemes to generate pseudo labels that will cause the gradual drift problem, or leverage consistency regularization methods which inevitably possess confirmation bias. To alleviate confirmation bias due to the lack of feedback loops in existing LRIE learning paradigms, we develop a Gradient Imitation Reinforcement Learning (GIRL) method to encourage pseudo-labeled data to imitate the gradient descent direction on labeled data, which can force pseudo-labeled data to achieve better optimization capabilities similar to labeled data. Based on how well the pseudo-labeled data imitates the instructive gradient descent direction obtained from labeled data, we design a reward to quantify the imitation process and bootstrap the optimization capability of pseudo-labeled data through trial and error. In addition to learning paradigms, GIRL is not limited to specific sub-tasks, and we leverage GIRL to solve all IE sub-tasks (named entity recognition, relation extraction, and event extraction) in low-resource settings (semi-supervised IE and few-shot IE).

最近，已经开发了各种视觉变压器作为对远程依赖性建模的能力。在当前的基于变压器的主骨用于医疗图像分割的骨架中，卷积层被纯变压器替换，或者将变压器添加到最深的编码器中以学习全球环境。但是，从规模的角度来看，主要有两个挑战：（1）尺度内问题：在每个尺度中提取局部全球线索所缺乏的现有方法，这可能会影响小物体的信号传播； （2）尺度间问题：现有方法未能从多个量表中探索独特的信息，这可能会阻碍表示尺寸，形状和位置广泛的对象的表示形式学习。为了解决这些局限性，我们提出了一个新颖的骨干，即比例尺形式，具有两个吸引人的设计：（1）尺度上的尺度内变压器旨在将基于CNN的本地功能与每个尺度中的基于变压器的全球线索相结合，在行和列的全局依赖项上可以通过轻巧的双轴MSA提取。 （2）一种简单有效的空间感知尺度变压器旨在以多个尺度之间的共识区域相互作用，该区域可以突出跨尺度依赖性并解决复杂量表的变化。对不同基准测试的实验结果表明，我们的尺度形式的表现优于当前最新方法。该代码可公开可用：https：//github.com/zjugivelab/scaleformer。

张量分解已被证明可以有效分析多维数据的结构。但是，这些方法中的大多数都需要一个关键参数：所需组件的数量。在CandeComp/Parafac分解（CPD）的情况下，该值称为规范等级，并极大地影响了结果的质量。现有方法使用启发式方法或贝叶斯方法通过反复计算CPD来估计该值，从而使它们在计算上非常昂贵。在这项工作中，我们提出了Frappe和Frappe：一种便宜的监督和一种自制的方法，可以估算张量的规范等级，而无需计算CPD。我们称坦率的监督称为Frappe，因为它使用了完全合成的训练集，而无需实际示例。我们评估了这些方法关于合成张量，已知等级的实际张量和卷积神经网络的重量张量的评估。我们表明，Frappe和Fell-Frappe在有效性和速度方面都提供了巨大的提高，各自的$ 15 \％$ $和$ 10 \％$ $ $ $ $ $改善的MAPE和$ 4000 \ times $和$ 13 \ times $ \ times $ $提高评估速度超过了最佳的评估速度 - 绩效基线。

虽然U-Net在医学图像分割任务中取得了巨大的成功，但它缺乏明确模拟远程依赖性的能力。因此，视觉变压器最近被出现为替代分割结构，以便通过自我关注捕获远程相关性的先天能力（SA）。然而，变压器通常依赖于大规模的预训练并具有高的计算复杂性。此外，SA只能在单个样本内模拟自我亲和力，忽略整个数据集的潜在相关性。为了解决这些问题，我们提出了一种名为混合变压器模块（MTM）的新型变压器模块，用于同时和内部内部学习。 MTM首先通过我们设计精心设计的本地全球高斯加权自我关注（LGG-SA），有效地计算自我亲创。然后，它通过外部注意力（EA）挖掘数据样本之间的连接。通过使用MTM，我们构造一个名为混合变压器U-NET（MT-UNET）的U形模型，以进行准确的医学图像分割。我们在两个不同的公共数据集上测试我们的方法，实验结果表明，该方法达到了更好的性能，对其他最先进的方法进行了更好的性能。代码可在：https://github.com/dootmaan/mt-unet。

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.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.