异常检测任务在AI安全中起着至关重要的作用。处理这项任务存在巨大的挑战。观察结果表明，深度神经网络分类器通常倾向于以高信心将分布（OOD）输入分为分配类别。现有的工作试图通过在培训期间向分类器暴露于分类器时明确对分类器施加不确定性来解决问题。在本文中，我们提出了一种替代概率范式，该范式实际上对OOD检测任务既有用，又可行。特别是，我们在培训过程中施加了近距离和离群数据之间的统计独立性，以确保inlier数据在培训期间向深度估计器显示有关OOD数据的信息很少。具体而言，我们通过Hilbert-Schmidt独立标准（HSIC）估算了Inlier和离群数据之间的统计依赖性，并在培训期间对此类度量进行了惩罚。我们还将方法与推理期间的新型统计测试相关联，加上我们的原则动机。经验结果表明，我们的方法对各种基准测试的OOD检测是有效且可靠的。与SOTA模型相比，我们的方法在FPR95，AUROC和AUPR指标方面取得了重大改进。代码可用：\ url {https://github.com/jylins/hone}。

大多数现有的基于学习的图像匹配管道都是为更好的特征探测器和描述符而设计的，这些探测器和描述符对重复纹理，观点更改等非常可靠，而对旋转不变性的关注很少。结果，由于缺乏关键点方向预测，这些方法通常表现出与手工制作的算法相比，与手工制作的算法相比，数据的性能较低。为了有效地解决该问题，提出了一种基于知识蒸馏的方法来改善旋转稳健性，而无需额外的计算成本。具体而言，基于基本模型，我们提出了多方面的特征聚合（MOFA），后来被用作蒸馏管道中的教师。此外，将旋转的内核融合（RKF）应用于学生模型的每个卷积内核，以促进学习旋转不变的特征。最终，实验表明，我们的建议可以在各种旋转下成功概括，而在推理阶段无需额外成本。

本文报告了NTIRE 2022关于感知图像质量评估（IQA）的挑战，并与CVPR 2022的图像恢复和增强研讨会（NTIRE）研讨会（NTIRE）讲习班的新趋势举行。感知图像处理算法。这些算法的输出图像与传统扭曲具有完全不同的特征，并包含在此挑战中使用的PIP数据集中。这个挑战分为两条曲目，一个类似于以前的NTIRE IQA挑战的全参考IQA轨道，以及一条侧重于No-Reference IQA方法的新曲目。挑战有192和179名注册参与者的两条曲目。在最后的测试阶段，有7和8个参与的团队提交了模型和事实表。几乎所有这些都比现有的IQA方法取得了更好的结果，并且获胜方法可以证明最先进的性能。

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。

本文回顾了关于压缩视频质量增强质量的第一个NTIRE挑战，重点是拟议的方法和结果。在此挑战中，采用了新的大型不同视频（LDV）数据集。挑战有三个曲目。Track 1和2的目标是增强HEVC在固定QP上压缩的视频，而Track 3旨在增强X265压缩的视频，以固定的位速率压缩。此外，轨道1和3的质量提高了提高保真度（PSNR）的目标，以及提高感知质量的2个目标。这三个曲目完全吸引了482个注册。在测试阶段，分别提交了12个团队，8支球队和11支球队，分别提交了轨道1、2和3的最终结果。拟议的方法和解决方案衡量视频质量增强的最先进。挑战的首页：https：//github.com/renyang-home/ntire21_venh

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.

Inferring missing links or detecting spurious ones based on observed graphs, known as link prediction, is a long-standing challenge in graph data analysis. With the recent advances in deep learning, graph neural networks have been used for link prediction and have achieved state-of-the-art performance. Nevertheless, existing methods developed for this purpose are typically discriminative, computing features of local subgraphs around two neighboring nodes and predicting potential links between them from the perspective of subgraph classification. In this formalism, the selection of enclosing subgraphs and heuristic structural features for subgraph classification significantly affects the performance of the methods. To overcome this limitation, this paper proposes a novel and radically different link prediction algorithm based on the network reconstruction theory, called GraphLP. Instead of sampling positive and negative links and heuristically computing the features of their enclosing subgraphs, GraphLP utilizes the feature learning ability of deep-learning models to automatically extract the structural patterns of graphs for link prediction under the assumption that real-world graphs are not locally isolated. Moreover, GraphLP explores high-order connectivity patterns to utilize the hierarchical organizational structures of graphs for link prediction. Our experimental results on all common benchmark datasets from different applications demonstrate that the proposed method consistently outperforms other state-of-the-art methods. Unlike the discriminative neural network models used for link prediction, GraphLP is generative, which provides a new paradigm for neural-network-based link prediction.

Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned. Examinations of learning frameworks for invariance have seldom been looked into. In this work, we review rotation invariance in terms of point cloud registration and propose an effective framework for rotation invariance learning via three sequential stages, namely rotation-invariant shape encoding, aligned feature integration, and deep feature registration. We first encode shape descriptors constructed with respect to reference frames defined over different scales, e.g., local patches and global topology, to generate rotation-invariant latent shape codes. Within the integration stage, we propose Aligned Integration Transformer to produce a discriminative feature representation by integrating point-wise self- and cross-relations established within the shape codes. Meanwhile, we adopt rigid transformations between reference frames to align the shape codes for feature consistency across different scales. Finally, the deep integrated feature is registered to both rotation-invariant shape codes to maximize feature similarities, such that rotation invariance of the integrated feature is preserved and shared semantic information is implicitly extracted from shape codes. Experimental results on 3D shape classification, part segmentation, and retrieval tasks prove the feasibility of our work. Our project page is released at: https://rotation3d.github.io/.

With the attention mechanism, transformers achieve significant empirical successes. Despite the intuitive understanding that transformers perform relational inference over long sequences to produce desirable representations, we lack a rigorous theory on how the attention mechanism achieves it. In particular, several intriguing questions remain open: (a) What makes a desirable representation? (b) How does the attention mechanism infer the desirable representation within the forward pass? (c) How does a pretraining procedure learn to infer the desirable representation through the backward pass? We observe that, as is the case in BERT and ViT, input tokens are often exchangeable since they already include positional encodings. The notion of exchangeability induces a latent variable model that is invariant to input sizes, which enables our theoretical analysis. - To answer (a) on representation, we establish the existence of a sufficient and minimal representation of input tokens. In particular, such a representation instantiates the posterior distribution of the latent variable given input tokens, which plays a central role in predicting output labels and solving downstream tasks. - To answer (b) on inference, we prove that attention with the desired parameter infers the latent posterior up to an approximation error, which is decreasing in input sizes. In detail, we quantify how attention approximates the conditional mean of the value given the key, which characterizes how it performs relational inference over long sequences. - To answer (c) on learning, we prove that both supervised and self-supervised objectives allow empirical risk minimization to learn the desired parameter up to a generalization error, which is independent of input sizes. Particularly, in the self-supervised setting, we identify a condition number that is pivotal to solving downstream tasks.