近年来，破坏预测取得了迅速的进展，尤其是在机器学习（ML）的方法中。理解为什么预测因子使某个预测与未来Tokamak破坏预测指标的预测准确性一样至关重要。大多数破坏预测因素的目的是准确性或跨机能力。但是，如果可以解释中断预测模型，则可以说明为什么某些样品被归类为中断前体。这使我们能够说出传入的破坏类型，并使我们深入了解破坏机制。本文根据J-TEXT上的物理引导特征提取（IDP-PGFE）设计了一种称为可解释的破坏预测变量的破坏预测变量。通过提取物理引导的特征有效地改善了模型的预测性能。需要高性能模型来确保解释结果的有效性。 IDP-PGFE的可解释性研究提供了对J-Text破坏的理解，并且通常与现有的破坏理解一致。 IDP-PGFE已被应用于破坏，因为在J文本上的密度极限实验的密度不断增加。 PGFE的时间演变具有贡献，表明ECRH的应用触发了辐射引起的破坏，从而降低了破坏时的密度。虽然RMP的应用确实提高了J文本中的密度极限。解释性研究指导了RMP不仅会影响MHD不稳定性，而且还会影响辐射轮廓的密度极限破坏的物理机制，从而延迟了密度极限的破坏。

深度学习方法论为高光谱图像（HSI）分析社区的发展做出了很大贡献。但是，这也使HSI分析系统容易受到对抗攻击的影响。为此，我们在本文中提出了一个掩盖的空间光谱自动编码器（MSSA），根据自我监督的学习理论，以增强HSI分析系统的鲁棒性。首先，进行了一个掩盖的序列注意学习模块，以促进沿光谱通道的HSI分析系统的固有鲁棒性。然后，我们开发了一个具有可学习的图形结构的图形卷积网络，以建立全局像素的组合。这样，每种组合中的所有相关像素都可以分散攻击效果，并且在空间方面可以实现更好的防御性能。最后，为了提高防御能力并解决有限标记样品的问题，MSSA采用光谱重建作为借口任务，并以自我监督的方式适合数据集。 - 高光谱分类方法和代表性的对抗防御策略。

膝关节骨关节炎（OA）是最常见的骨关节炎和伤残原因。软骨缺陷被认为是膝关节OA的主要表现，其通过磁共振成像（MRI）可见。因此，对膝关节软骨缺陷的早期检测和评估对于保护膝关节OA患者来说是重要的。通过这种方式，通过将卷积神经网络（CNNS）应用于膝关节MRI，已经在膝关节软骨缺陷评估中进行了许多尝试。然而，软骨的生理特性可能阻碍这种努力：软骨是薄的弯曲层，这意味着只有膝关节MRI中的一小部分体素可以有助于软骨缺陷评估;异构扫描方案进一步挑战CNN在临床实践中的可行性;基于CNN的膝关节软骨评估结果缺乏解释性。为了解决这些挑战，我们将软骨结构和外观模拟到膝关节MRI进入图表表示，该图表能够处理高度多样化的临床数据。然后，由软骨图表示指导，我们设计了一种具有自我关注机制的非欧几里德深度学习网络，提取本地和全局中的软骨功能，并通过可视化结果导出最终评估。我们的综合实验表明，该方法在膝关节软骨缺陷评估中产生了卓越的性能，以及其方便的可解释性3D可视化。

病变检测是乳房X线照相术的计算机辅助诊断方案中的一个基本问题。如果培训数据在图像风格和质量方面，深度学习技术的进步对这项任务产生了显着的进展。特别地，图像样式的多样性可能主要归因于供应商因子。然而，尽可能多的供应商收集来自供应商的非常昂贵，并且有时对于实验室规模研究是不切实际的。因此，为了进一步将深度学习模型的泛化能力扩展到具有有限资源有限的各种供应商，开发了一种新的对比学习方案。具体地，骨干网络首先具有多种式和多视图无监督的自学习方案，用于将不变功能嵌入到各种供应商样式中。之后，用特定的监督学习重新校准骨干网络与病变检测的下游任务。所提出的方法是用来自四个供应商的乳房X线照片和一个看不见的公共数据集进行评估。实验结果表明，我们的方法可以有效地改善观察和看不见的域的检测性能，并且优于许多最先进的（SOTA）泛化方法。

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.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets.

Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and ({even more importantly}) explainable predictions. Existing approaches typically explicate network outputs in a post-hoc fashion, under an implicit assumption that faithful explanations come from accurate predictions/classifications. We have an opposite claim that explanations boost (or even determine) classification. That is, end-to-end learning of explanation factors to augment discriminative representation extraction could be a more intuitive strategy to inversely assure fine-grained explainability, e.g., in those neuroimaging and neuroscience studies with high-dimensional data containing noisy, redundant, and task-irrelevant information. In this paper, we propose such an explainable geometric deep network dubbed as NeuroExplainer, with applications to uncover altered infant cortical development patterns associated with preterm birth. Given fundamental cortical attributes as network input, our NeuroExplainer adopts a hierarchical attention-decoding framework to learn fine-grained attentions and respective discriminative representations to accurately recognize preterm infants from term-born infants at term-equivalent age. NeuroExplainer learns the hierarchical attention-decoding modules under subject-level weak supervision coupled with targeted regularizers deduced from domain knowledge regarding brain development. These prior-guided constraints implicitly maximizes the explainability metrics (i.e., fidelity, sparsity, and stability) in network training, driving the learned network to output detailed explanations and accurate classifications. Experimental results on the public dHCP benchmark suggest that NeuroExplainer led to quantitatively reliable explanation results that are qualitatively consistent with representative neuroimaging studies.

Although deep learning has made remarkable progress in processing various types of data such as images, text and speech, they are known to be susceptible to adversarial perturbations: perturbations specifically designed and added to the input to make the target model produce erroneous output. Most of the existing studies on generating adversarial perturbations attempt to perturb the entire input indiscriminately. In this paper, we propose ExploreADV, a general and flexible adversarial attack system that is capable of modeling regional and imperceptible attacks, allowing users to explore various kinds of adversarial examples as needed. We adapt and combine two existing boundary attack methods, DeepFool and Brendel\&Bethge Attack, and propose a mask-constrained adversarial attack system, which generates minimal adversarial perturbations under the pixel-level constraints, namely ``mask-constraints''. We study different ways of generating such mask-constraints considering the variance and importance of the input features, and show that our adversarial attack system offers users good flexibility to focus on sub-regions of inputs, explore imperceptible perturbations and understand the vulnerability of pixels/regions to adversarial attacks. We demonstrate our system to be effective based on extensive experiments and user study.