训练深图神经网络（GNNS）构成了一项具有挑战性的任务，因为GNN的性能可能会遭受隐藏的消息层的数量。文献集中在过度平滑和了解深度GNN的性能恶化的建议上。在本文中，我们提出了一种新的解释，以解决这种恶化的性能现象，即错误的简化，也就是说，通过防止自我浮动和强迫不得加权的边缘来简化图形。我们表明，这种简化可以降低消息通话层的潜力以捕获图的结构信息。鉴于此，我们提出了一个新的框架，Edge增强了图形神经网络（EEGNN）。 EEGNN使用从提出的Dirichlet混合泊松图模型（贝叶斯非参数模型）中提取的结构信息，以改善各种深度消息的GNN的性能。不同数据集的实验表明，与基准相比，我们的方法实现了可观的性能。

逐渐射击的语义分割（IFSS）目标以逐步扩展模型的能力逐渐扩大了仅由几个样本监督的新图像。但是，在旧课程中学到的特征可能会大大漂移，从而导致灾难性遗忘。此外，很少有针对新课程的像素级细分样本会导致每个学习课程中臭名昭著的过度拟合问题。在本文中，我们明确表示基于类别的语义分割的知识作为类别嵌入和超级类嵌入，前者描述了独家的语义属性，而后者则表示超级类知识作为类共享语义属性。为了解决IFSS问题，我们提出了EHNET，即从两个方面嵌入自适应更高和超级级表示网络。首先，我们提出了一种嵌入自适应的策略，以避免特征漂移，该策略通过超级班级表示保持旧知识，并使用类似课程的方案自适应地更新类别嵌入类别，以涉及在各个会话中学习的新课程。其次，为了抵制很少有培训样本引起的过度拟合问题，通过将所有类别嵌入以进行初始化并与新班级的类别保持一致以进行增强，从而学习了超级班级的嵌入，从而使学会知识有助于学习新知识，从而减轻了绩效绩效的绩效，依赖培训数据量表。值得注意的是，这两种设计为具有足够语义和有限偏见的类提供了表示能力，从而可以执行需要高语义依赖性的分割任务。 Pascal-5i和可可数据集的实验表明，EHNET具有显着优势的新最先进的性能。

通过恢复（实体瘤的响应评估标准）自动测量病变/肿瘤大小，直径和分割对于计算机辅助诊断很重要。尽管近年来已经研究了它，但仍有空间可以提高其准确性和鲁棒性，例如（1）通过合并丰富的上下文信息来增强功能，同时保持高空间分辨率，（2）涉及新任务和损失以进行关节优化。为了实现这一目标，本文提出了一个基于变压器的网络（Meaformer，测量变压器），用于病变恢复直径预测和分割（LRDPS）。它被配制为三个相关和互补任务：病变分割，热图预测和关键点回归。据我们所知，这是首次使用按键重点回归进行恢复直径预测。 MeaeFormer可以通过使用变压器来捕获其远程依赖性来增强高分辨率功能。引入了两个一致性损失，以明确建立这些任务之间的关系，以更好地优化。实验表明，MeAformer实现了LRDP在大规模深层数据集上的最新性能，并在纵向研究中产生了两个下游诊所的任务，即3D病变细分和恢复评估。

为了应对人类检测对标签数据和隐私问题的不断增长的需求，合成数据已被用作替代品，并在人类检测和跟踪任务中显示出令人鼓舞的结果。我们参加了第七届基准测试多目标跟踪（BMTT）的研讨会，主题是“合成数据可以带我们多远”？我们的解决方案Pietrack是根据合成数据开发的，而无需使用任何预训练的权重。我们提出了一种自我监督的域适应方法，该方法能够减轻合成（例如Motsynth）和真实数据（例如Mot17）之间的域移位问题，而无需涉及额外的人类标签。通过利用拟议的多尺度合奏推理，我们在MOT17测试集中获得了58.7的最终HOTA得分，在挑战中排名第三。

膝关节骨关节炎（OA）是一种常见的堕落联合障碍，影响全世界的大型老年人。膝关节OA严重程度的准确放射线摄影评估在慢性患者管理中起着关键作用。目前临床采用的膝盖oA分级系统是观察者主观的，遭受帧间间的分歧。在这项工作中，我们提出了一种计算机辅助诊断方法，可以同时为两种复合材料和细粒度的OA等级提供更准确和一致的评估。提出了一种新的半监督学习方法，通过从未标记的数据学习来利用复合材料和细粒度的OA等级的潜在一致性。通过使用预先训练的高斯混合模型的日志概率表示等级相干性，我们制定了不连贯的损失，以纳入训练中的未标记数据。该方法还描述了基于关键点的汇集网络，其中从疾病目标键点（沿膝关节提取）汇集了深度图像特征，以提供更准确的和病于病理信息的特征表示，以获得准确的OA级评估。拟议的方法在公共骨关节炎倡议（OAI）数据上全面评估了4,796名科目的多中心的十年观测研究。实验结果表明，我们的方法对以前的强大的整个图像的深度分类网络基线（如Resnet-50）的显着改进。

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.