我们为平面姿势图优化提供了一个强大的框架，该框架被环闭合离群值污染。我们的框架首先将截短的最小二乘内核包裹的强大的PGO问题拒绝了异常值，从而拒绝了异常值。然后，该框架引入了线性角度表示，以重写最初用旋转矩阵配制的第一个子问题。该框架配置为渐变的非凸度（GNC）算法，以连续解决两个非凸子问题，而无需初始猜测。得益于两个子问题的线性属性，我们的框架只需要线性求解器才能最佳地解决GNC中遇到的优化问题。我们在平面PGO基准中广泛验证了所提出的框架，称为Degnc-Laf（脱钩的非跨性别量均具有线性角度公式）。事实证明，它比标准和通用GNC的速度显着（有时达到30倍以上），同时导致高质量的估计值。

恶意软件是跨越多个操作系统和各种文件格式的计算机的最损害威胁之一。为了防止不断增长的恶意软件的威胁，已经提出了巨大的努力来提出各种恶意软件检测方法，试图有效和有效地检测恶意软件。最近的研究表明，一方面，现有的ML和DL能够卓越地检测新出现和以前看不见的恶意软件。然而，另一方面，ML和DL模型本质上易于侵犯对抗性示例形式的对抗性攻击，这通过略微仔细地扰乱了合法输入来混淆目标模型来恶意地产生。基本上，在计算机视觉领域最初广泛地研究了对抗性攻击，并且一些快速扩展到其他域，包括NLP，语音识别甚至恶意软件检测。在本文中，我们专注于Windows操作系统系列中的便携式可执行文件（PE）文件格式的恶意软件，即Windows PE恶意软件，作为在这种对抗设置中研究对抗性攻击方法的代表性案例。具体而言，我们首先首先概述基于ML / DL的Windows PE恶意软件检测的一般学习框架，随后突出了在PE恶意软件的上下文中执行对抗性攻击的三个独特挑战。然后，我们进行全面和系统的审查，以对PE恶意软件检测以及增加PE恶意软件检测的稳健性的相应防御，对近最新的对手攻击进行分类。我们首先向Windows PE恶意软件检测的其他相关攻击结束除了对抗对抗攻击之外，然后对未来的研究方向和机遇脱落。

Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader domains and tasks, there is a need to establish richer and more diverse benchmarks to better reflect the reality of the application scenarios. Graph learning is an emerging field of machine learning that urgently needs more and better benchmarks. To accommodate the need, we introduce Graph Learning Indexer (GLI), a benchmark curation platform for graph learning. In comparison to existing graph learning benchmark libraries, GLI highlights two novel design objectives. First, GLI is designed to incentivize \emph{dataset contributors}. In particular, we incorporate various measures to minimize the effort of contributing and maintaining a dataset, increase the usability of the contributed dataset, as well as encourage attributions to different contributors of the dataset. Second, GLI is designed to curate a knowledge base, instead of a plain collection, of benchmark datasets. We use multiple sources of meta information to augment the benchmark datasets with \emph{rich characteristics}, so that they can be easily selected and used in downstream research or development. The source code of GLI is available at \url{https://github.com/Graph-Learning-Benchmarks/gli}.

逆势培训可针对特异性对抗性扰动有用，但它们也证明旨在展示偏离用于培训的攻击的攻击。然而，我们观察到这种无效性是本质上与域的适应性，深度学习中的另一个关键问题似乎是一个有希望的解决方案。因此，我们提出了ADV-4-ADV作为一种新的逆势培训方法，旨在保持针对看不见的对抗性扰动的鲁棒性。基本上，ADV-4-ADV将攻击产生不同的扰动作为不同的域，并且通过利用逆势域适应的力量，它旨在消除域/攻击特定的功能。这迫使训练有素的模型来学习强大的域名不变的表示，这反过来增强了其泛化能力。对时尚 - MNIST，SVHN，CIFAR-10和CIFAR-100的广泛评估表明，基于由简单攻击（例如，FGSM）制备的样本训练的模型可以推广到更高级的攻击（例如， PGD​​），性能超过了这些数据集的最先进的提案。

对医疗保健和生物医学应用的关键，呼吸监测通常在实践中使用可穿戴传感器，由于它们与人体直接接触而导致不便。因此，研究人员一直在不断寻找免费的接触替代品。尽管如此，现有的无联系设计主要需要人类受试者保持静止，在正常环境中大大限制了身体运动不可避免的日常环境中的收养。幸运的是，透射频率（RF）使能无接触感测，但通过传统过滤不可分割的运动干扰，可以在深度学习的帮助下提供蒸馏呼吸波形的潜力。为了实现这一潜力，我们在身体运动下引入了更多内容以进行细粒度的呼吸监测。更多-fi利用IR-UWB雷达来实现无接触感测，并充分利用复杂的雷达信号进行数据增强。更多-Fi的核心是一种新颖的变分编码器解码器网络;它旨在单独用以非线性方式通过身体运动调节的呼吸波形。我们具有12个受试者和66小时数据的实验表明，尽管身体运动引起的干扰，但仍然需要更准确地恢复呼吸波。我们还讨论了肺部疾病诊断的潜在应用。

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.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover instances of novel classes. Rather than adopting pretrained caption models or using massive caption datasets with complex pipelines, we propose an end-to-end solution from two aspects: caption grounding and caption generation. In particular, we devise a joint Caption Grounding and Generation (CGG) framework based on a Mask Transformer baseline. The framework has a novel grounding loss that performs explicit and implicit multi-modal feature alignments. We further design a lightweight caption generation head to allow for additional caption supervision. We find that grounding and generation complement each other, significantly enhancing the segmentation performance for novel categories. We conduct extensive experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS). The results demonstrate the superiority of our CGG framework over previous OVIS methods, achieving a large improvement of 6.8% mAP on novel classes without extra caption data. Our method also achieves over 15% PQ improvements for novel classes on the OSPS benchmark under various settings.

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.