The statistical heterogeneity of the non-independent and identically distributed (non-IID) data in local clients significantly limits the performance of federated learning. Previous attempts like FedProx, SCAFFOLD, MOON, FedNova and FedDyn resort to an optimization perspective, which requires an auxiliary term or re-weights local updates to calibrate the learning bias or the objective inconsistency. However, in addition to previous explorations for improvement in federated averaging, our analysis shows that another critical bottleneck is the poorer optima of client models in more heterogeneous conditions. We thus introduce a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices. We provide theoretical analysis of the possible benefit from FedSkip and conduct extensive experiments on a range of datasets to demonstrate that FedSkip achieves much higher accuracy, better aggregation efficiency and competing communication efficiency. Source code is available at: https://github.com/MediaBrain-SJTU/FedSkip.

The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competition, Autonomous Driving Security Competition, and Face Recognition Security Competition. This report will introduce the competition rules of these three tracks and the solutions of top-ranking teams in each track.

Zero-sum Markov Games (MGs) has been an efficient framework for multi-agent systems and robust control, wherein a minimax problem is constructed to solve the equilibrium policies. At present, this formulation is well studied under tabular settings wherein the maximum operator is primarily and exactly solved to calculate the worst-case value function. However, it is non-trivial to extend such methods to handle complex tasks, as finding the maximum over large-scale action spaces is usually cumbersome. In this paper, we propose the smoothing policy iteration (SPI) algorithm to solve the zero-sum MGs approximately, where the maximum operator is replaced by the weighted LogSumExp (WLSE) function to obtain the nearly optimal equilibrium policies. Specially, the adversarial policy is served as the weight function to enable an efficient sampling over action spaces.We also prove the convergence of SPI and analyze its approximation error in $\infty -$norm based on the contraction mapping theorem. Besides, we propose a model-based algorithm called Smooth adversarial Actor-critic (SaAC) by extending SPI with the function approximations. The target value related to WLSE function is evaluated by the sampled trajectories and then mean square error is constructed to optimize the value function, and the gradient-ascent-descent methods are adopted to optimize the protagonist and adversarial policies jointly. In addition, we incorporate the reparameterization technique in model-based gradient back-propagation to prevent the gradient vanishing due to sampling from the stochastic policies. We verify our algorithm in both tabular and function approximation settings. Results show that SPI can approximate the worst-case value function with a high accuracy and SaAC can stabilize the training process and improve the adversarial robustness in a large margin.

深度学习取得了长足的进步，用于图像中的对象检测。对象检测的检测准确性和计算成本取决于图像的空间分辨率，这可能会受到相机和存储注意事项的约束。压缩通常是通过减少空间或幅度分辨率或有时两者都对性能的众所周知的影响来实现的。检测精度还取决于感兴趣的对象与摄像机的距离。我们的工作研究了空间和振幅分辨率以及对象距离对物体检测准确性和计算成本的影响。我们开发了Yolov5（ra-Yolo）的分辨率 - 自适应变体，该变体基于输入图像的空间分辨率，它在特征金字塔和检测头中变化。为了训练和评估这种新方法，我们通过结合TJU和Eurocity数据集的图像来创建具有不同空间和振幅分辨率的图像数据集，并通过应用空间调整和压缩来生成不同的分辨率。我们首先表明Ra-Yolo在各种空间分辨率上实现了检测准确性和推理时间之间的良好权衡。然后，我们使用拟议的RA-YOLO模型评估空间和振幅分辨率对物体检测准确性的影响。我们证明，导致最高检测精度的最佳空间分辨率取决于“耐受性”图像大小。我们进一步评估了对象到摄像机对检测准确性的影响，并表明较高的空间分辨率可实现更大的检测范围。这些结果为选择图像空间分辨率和压缩设置提供了重要的指南，这些分辨率和压缩设置基于可用的带宽，存储，所需的推理时间和/或所需的检测范围，在实际应用中。

最近，深层神经网络（DNNS）用于减少带宽并提高互联网视频交付的质量。现有的方法训练服务器上每个视频块的相应内容超级分辨率（SR）模型，并将低分辨率（LR）视频块以及SR模型一起流到客户端。尽管他们取得了令人鼓舞的结果，但网络培训的巨大计算成本限制了其实际应用。在本文中，我们提出了一种名为有效元调整（EMT）的方法，以降低计算成本。 EMT没有从头开始训练，而是将元学习的模型适应了输入视频的第一部分。至于以下块，它通过以前的改编模型的梯度掩盖选择了部分参数。为了实现EMT的进一步加速，我们提出了一种新颖的抽样策略，以从视频帧中提取最具挑战性的补丁。拟议的策略高效，带来了可忽略的额外成本。我们的方法大大降低了计算成本并取得更好的性能，为将神经视频传递技术应用于实际应用铺平了道路。我们基于各种有效的SR架构进行了广泛的实验，包括ESPCN，SRCNN，FSRCNN和EDSR-1，证明了我们工作的概括能力。该代码通过\ url {https://github.com/neural-video-delivery/emt-pytorch-eccv2022}发布。

典型的文本检测器遵循两阶段的发现策略：首先检测文本实例的精确边界，然后在定期的文本区域内执行文本识别。尽管这种策略取得了实质性进展，但有两个基本的局限性。 1）文本识别的性能在很大程度上取决于文本检测的精度，从而导致从检测到识别的潜在误差传播。 2）桥接检测和识别的ROI种植会带来背景的噪音，并在合并或从特征地图中插值时导致信息丢失。在这项工作中，我们提出了单个镜头自力更生的场景文本sottter（SRSTS），该场景通过将识别解除识别来规避这些限制。具体而言，我们并行进行文本检测和识别，并通过共享的积极锚点架起它们。因此，即使确切的文本边界要检测到具有挑战性，我们的方法也能够正确识别文本实例。此外，我们的方法可大大降低文本检测的注释成本。在常规基准和任意形状的基准上进行了广泛的实验表明，就准确性和效率而言，我们的SRST与以前的最先进的观察者相比有利。

监督字典学习（SDL）是一种经典的机器学习方法，同时寻求特征提取和分类任务，不一定是先验的目标。 SDL的目的是学习类歧视性词典，这是一组潜在特征向量，可以很好地解释特征以及观察到的数据的标签。在本文中，我们提供了SDL的系统研究，包括SDL的理论，算法和应用。首先，我们提供了一个新颖的框架，该框架将“提升” SDL作为组合因子空间中的凸问题，并提出了一种低级别的投影梯度下降算法，该算法将指数成倍收敛于目标的全局最小化器。我们还制定了SDL的生成模型，并根据高参数制度提供真实参数的全局估计保证。其次，我们被视为一个非convex约束优化问题，我们为SDL提供了有效的块坐标下降算法，该算法可以保证在$ O（\ varepsilon^{ - 1}（\ log）中找到$ \ varepsilon $ - 定位点（\ varepsilon \ varepsilon^{ - 1}）^{2}）$ iterations。对于相应的生成模型，我们为受约束和正则化的最大似然估计问题建立了一种新型的非反应局部一致性结果，这可能是独立的。第三，我们将SDL应用于监督主题建模和胸部X射线图像中的肺炎检测中，以进行不平衡的文档分类。我们还提供了模拟研究，以证明当最佳的重建性和最佳判别词典之间存在差异时，SDL变得更加有效。

先进的可穿戴设备越来越多地利用高分辨率多摄像头系统。作为用于处理所得到的图像数据的最先进的神经网络是计算要求的，对于利用第五代（5G）无线连接和移动边缘计算，已经越来越感兴趣，以将该处理卸载到云。为了评估这种可能性，本文提出了一个详细的仿真和评估，用于5G无线卸载，用于对象检测，在一个名为Vis4ion的强大新型智能可穿戴物中，用于盲目损害（BVI）。目前的Vis4ion系统是一种具有高分辨率摄像机，视觉处理和触觉和音频反馈的仪表簿。本文认为将相机数据上载到移动边缘云以执行实时对象检测并将检测结果传输回可穿戴。为了确定视频要求，纸张评估视频比特率和分辨率对物体检测精度和范围的影响。利用与BVI导航相关的标记对象的新街道场景数据集进行分析。视觉评估与详细的全堆栈无线网络仿真结合，以确定吞吐量的分布和延迟，具有来自城市环境中的新高分辨率3D模型的实际导航路径和射线跟踪。为了比较，无线仿真考虑了标准的4G长期演进（LTE）载波和高速度5G毫米波（MMWAVE）载波。因此，该工作提供了对具有高带宽和低延迟要求的应用中的MMWAVE连接的边缘计算的彻底和现实评估。

Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network model for scene text spotting is proposed. The proposed model, named as Mask TextSpotter, is inspired by the newly published work Mask R-CNN. Different from previous methods that also accomplish text spotting with end-to-end trainable deep neural networks, Mask TextSpotter takes advantage of simple and smooth end-to-end learning procedure, in which precise text detection and recognition are acquired via semantic segmentation. Moreover, it is superior to previous methods in handling text instances of irregular shapes, for example, curved text. Experiments on ICDAR2013, ICDAR2015 and Total-Text demonstrate that the proposed method achieves state-of-the-art results in both scene text detection and end-to-end text recognition tasks.

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.