故障诊断在许多领域至关重要，因为故障可能导致安全威胁或经济损失。在在线服务系统领域中，操作员依靠大量监视数据来检测和减轻故障。快速识别一组基础故障的根本原因指标可以节省大量时间减轻故障。在本文中，我们将根本原因分析问题作为一种新的因果推理任务，称为干预识别。我们提出了一种新型的无监督因果推理的方法，名为基于因果推理的根本原因分析（大约）。核心思想是一个足够的条件，可以使监视变量成为根本原因指标，即，因果关系贝叶斯网络（CBN）中父母的概率分布的变化。在在线服务系统中的应用程序中，大约根据系统体系结构的知识和一组因果假设在监视指标中构建图形。仿真研究说明了大约的理论可靠性。现实世界中数据集的性能进一步表明，大约可以将TOP-1建议的回忆提高到最佳基线方法的25％。

随着人工智能和机器学习的日益普及，文献中已经提出了针对深度学习模型的广泛攻击。逃避攻击和中毒攻击都试图利用对抗性变化的样本来欺骗受害者模型以错误地分类对抗样本。尽管这种攻击声称是隐形的，即对人的眼睛看不见，但很少评估这种说法。在本文中，我们介绍了第一个大规模研究，涉及对深度学习的攻击中使用的对抗样本的隐身性。我们已经对六个流行的基准数据集实施了20种代表性的对抗ML攻击。我们使用两种互补方法评估了攻击样本的隐身性：（1）一项数值研究，采用24个指标用于图像相似性或质量评估； （2）对3组问卷的用户研究，从1,000多个回答中收集了20,000多次注释。我们的结果表明，大多数现有攻击引入了不可忽略的扰动，这些扰动对人的眼睛并不隐秘。我们进一步分析了有助于攻击隐身性的因素。我们进一步研究了数值分析与用户研究之间的相关性，并证明某些图像质量指标可能在攻击设计中提供有用的指导，而评估的图像质量和攻击的视觉隐身性之间仍然存在显着差距。

磁共振图像（MRI）中的脑肿瘤分割（BTS）对于脑肿瘤诊断，癌症管理和研究目的至关重要。随着十年小型挑战的巨大成功以及CNN和Transformer算法的进步，已经提出了许多出色的BTS模型来解决BTS在不同技术方面的困难。但是，现有研究几乎没有考虑如何以合理的方式融合多模式图像。在本文中，我们利用了放射科医生如何从多种MRI模态诊断脑肿瘤的临床知识，并提出了一种称为CKD-TRANSBTS的临床知识驱动的脑肿瘤分割模型。我们没有直接串联所有模式，而是通过根据MRI的成像原理将输入方式分为两组来重新组织输入方式。具有拟议模态相关的跨意义块（MCCA）的双支支混合式编码器旨在提取多模式图像特征。所提出的模型以局部特征表示能力的能力来继承来自变压器和CNN的强度，以提供精确的病变边界和3D体积图像的远程特征提取。为了弥合变压器和CNN功能之间的间隙，我们提出了解码器中的反式和CNN功能校准块（TCFC）。我们将提出的模型与五个基于CNN的模型和六个基于Transformer的模型在Brats 2021挑战数据集上进行了比较。广泛的实验表明，与所有竞争对手相比，所提出的模型可实现最先进的脑肿瘤分割性能。

超声检查是乳腺癌诊断的重要常规检查，这是由于其无创，无辐射和低成本的特性。但是，由于其固有的局限性，乳腺癌的诊断准确性仍然受到限制。如果我们可以通过乳房超声图像（BUS）精确诊断乳腺癌，那将是一个巨大的成功。已经提出了许多基于学习的计算机辅助诊断方法来实现乳腺癌诊断/病变分类。但是，其中大多数需要预定的ROI，然后对ROI内的病变进行分类。常规的分类骨架，例如VGG16和RESNET50，可以在没有ROI要求的情况下获得有希望的分类结果。但是这些模型缺乏解释性，因此限制了它们在临床实践中的使用。在这项研究中，我们提出了一种具有可解释特征表示的超声图像中乳腺癌诊断的新型无ROI模型。我们利用解剖学的先验知识，即恶性肿瘤和良性肿瘤在不同的组织层之间具有不同的空间关系，并提出了悬停转换器来提出这种先验知识。提出的悬停式跨界块水平和垂直地提取层间和层内空间信息。我们进行并释放一个开放的数据集GDPH＆SYSUCC，以用于公共汽车中的乳腺癌诊断。通过与四个基于CNN的模型和两个Vision Transformer模型进行比较，通过五倍的交叉验证来评估所提出的模型。它通过最佳模型可解释性实现最新的分类性能。同时，我们提出的模型在仅给出一张公交图像时，在乳腺癌诊断方面优于两名高级超声检查员。

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.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.

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.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

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.