我们提出了Styletalker，这是一种新颖的音频驱动的会说话的头部生成模型，可以从单个参考图像中综合一个会说话的人的视频，并具有准确的音频同步的唇形，逼真的头姿势和眼睛眨眼。具体而言，通过利用预验证的图像生成器和图像编码器，我们估计了会说话的头视频的潜在代码，这些代码忠实地反映了给定的音频。通过几个新设计的组件使这成为可能：1）一种用于准确唇部同步的对比性唇部同步鉴别剂，2）一种条件顺序的连续变异自动编码器，该差异自动编码器了解从唇部运动中解散的潜在运动空间，以便我们可以独立地操纵运动运动的运动。和唇部运动，同时保留身份。 3）自动回归事先增强，并通过标准化流量来学习复杂的音频到运动多模式潜在空间。配备了这些组件，Styletalker不仅可以在给出另一个运动源视频时以动作控制的方式生成说话的头视频，而且还可以通过从输入音频中推断出现实的动作，以完全由音频驱动的方式生成。通过广泛的实验和用户研究，我们表明我们的模型能够以令人印象深刻的感知质量合成会说话的头部视频，这些视频与输入音频相符，可以准确地唇部同步，这在很大程度上要优于先进的基线。

实时视频细分是许多实际应用程序（例如自动驾驶和机器人控制）的关键任务。由于最新的语义细分模型尽管表现令人印象深刻，但对于实时应用来说通常太重了，因此研究人员提出了具有速度准确性权衡的轻量级体系结构，以降低准确性为代价实现实时速度。在本文中，我们提出了一个新颖的框架，通过利用视频中的时间位置来加快使用跳过连接进行实时视觉任务的架构。具体而言，在每个帧的到来时，我们将特征从上一个帧转换为在特定的空间箱中重复使用它们。然后，我们在当前帧区域上对骨干网络进行部分计算，以捕获当前帧和上一个帧之间的时间差异。这是通过使用门控机制动态掉出残留块来完成的，该机制决定哪些基于框架间失真掉落。我们在具有多个骨干网络的视频语义分割基准上验证了我们的时空掩码发生器（STMG），并证明我们的方法在很大程度上可以随着准确性的最小损失而加快推断。

该技术报告介绍了AQTC的第二次获胜模型，AQTC是CVPR 2022年长期视频理解（LOVEU）挑战中新引入的任务。这项挑战在视频中的多模式答案，多模式以及多样化和不断变化的按钮表示面临困难。我们通过提出一种新的上下文地面模块注意机制来解决此问题，以进行更有效的功能映射。此外，我们还对不同步骤网络和视频功能的按钮数量和消融研究进行分析。结果，我们获得了Loveu竞赛曲目3中的总排名，特别是四分之二评估指标中的第一名。我们的代码可在https://github.com/jaykim9870/ cvpr-22_loveu_unipyler上找到。

本文介绍了一个混合运动规划策略，将深度生成网络与传统运动规划方法相结合。现有的规划方法如* *和混合动力A *被广泛用于路径规划任务，因为它们即使在复杂的环境中确定可行的路径也是如此;但是，它们对效率有局限性。为了克服这些限制，介绍了一种基于神经网络的路径规划算法，即神经混合A *。本文使用条件变形Autiachoder（CVAE）提出通过利用CVAE在给定停车环境的信息的情况下学习有关规划空间信息的能力来指导搜索算法。基于在示威中学到的可行轨迹的分布，利用了非均匀的扩展策略。该方法有效地学习给定状态的表示，并显示了算法性能方面的改进。

The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Computing (MEC). Diverse user behaviors call for personalized services with heterogeneous Machine Learning (ML) models on different devices. Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC. Introducing knowledge distillation into FMTL can simultaneously enable efficient communication and model heterogeneity among clients, whereas existing methods rely on a public dataset, which is impractical in reality. To tackle this dilemma, Federated MultI-task Distillation for Multi-access Edge CompuTing (FedICT) is proposed. FedICT direct local-global knowledge aloof during bi-directional distillation processes between clients and the server, aiming to enable multi-task clients while alleviating client drift derived from divergent optimization directions of client-side local models. Specifically, FedICT includes Federated Prior Knowledge Distillation (FPKD) and Local Knowledge Adjustment (LKA). FPKD is proposed to reinforce the clients' fitting of local data by introducing prior knowledge of local data distributions. Moreover, LKA is proposed to correct the distillation loss of the server, making the transferred local knowledge better match the generalized representation. Experiments on three datasets show that FedICT significantly outperforms all compared benchmarks in various data heterogeneous and model architecture settings, achieving improved accuracy with less than 1.2% training communication overhead compared with FedAvg and no more than 75% training communication round compared with FedGKT.

In this paper we study the smooth strongly convex minimization problem $\min_{x}\min_y f(x,y)$. The existing optimal first-order methods require $\mathcal{O}(\sqrt{\max\{\kappa_x,\kappa_y\}} \log 1/\epsilon)$ of computations of both $\nabla_x f(x,y)$ and $\nabla_y f(x,y)$, where $\kappa_x$ and $\kappa_y$ are condition numbers with respect to variable blocks $x$ and $y$. We propose a new algorithm that only requires $\mathcal{O}(\sqrt{\kappa_x} \log 1/\epsilon)$ of computations of $\nabla_x f(x,y)$ and $\mathcal{O}(\sqrt{\kappa_y} \log 1/\epsilon)$ computations of $\nabla_y f(x,y)$. In some applications $\kappa_x \gg \kappa_y$, and computation of $\nabla_y f(x,y)$ is significantly cheaper than computation of $\nabla_x f(x,y)$. In this case, our algorithm substantially outperforms the existing state-of-the-art methods.

Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data to identify an optimal feature space that enhances the performances of a downstream ML model. Existing studies either combines preprocessing, feature selection, and generation skills to empirically transform data, or automate feature transformation by machine intelligence, such as reinforcement learning. However, existing studies suffer from: 1) high-dimensional non-discriminative feature space; 2) inability to represent complex situational states; 3) inefficiency in integrating local and global feature information. To fill the research gap, we formulate the feature transformation task as an iterative, nested process of feature generation and selection, where feature generation is to generate and add new features based on original features, and feature selection is to remove redundant features to control the size of feature space. Finally, we present extensive experiments and case studies to illustrate 24.7\% improvements in F1 scores compared with SOTAs and robustness in high-dimensional data.

In recent years, large amounts of effort have been put into pushing forward the real-world application of dynamic digital human (DDH). However, most current quality assessment research focuses on evaluating static 3D models and usually ignores motion distortions. Therefore, in this paper, we construct a large-scale dynamic digital human quality assessment (DDH-QA) database with diverse motion content as well as multiple distortions to comprehensively study the perceptual quality of DDHs. Both model-based distortion (noise, compression) and motion-based distortion (binding error, motion unnaturalness) are taken into consideration. Ten types of common motion are employed to drive the DDHs and a total of 800 DDHs are generated in the end. Afterward, we render the video sequences of the distorted DDHs as the evaluation media and carry out a well-controlled subjective experiment. Then a benchmark experiment is conducted with the state-of-the-art video quality assessment (VQA) methods and the experimental results show that existing VQA methods are limited in assessing the perceptual loss of DDHs. The database will be made publicly available to facilitate future research.

Recently, over-height vehicle strike frequently occurs, causing great economic cost and serious safety problems. Hence, an alert system which can accurately discover any possible height limiting devices in advance is necessary to be employed in modern large or medium sized cars, such as touring cars. Detecting and estimating the height limiting devices act as the key point of a successful height limit alert system. Though there are some works research height limit estimation, existing methods are either too computational expensive or not accurate enough. In this paper, we propose a novel stereo-based pipeline named SHLE for height limit estimation. Our SHLE pipeline consists of two stages. In stage 1, a novel devices detection and tracking scheme is introduced, which accurately locate the height limit devices in the left or right image. Then, in stage 2, the depth is temporally measured, extracted and filtered to calculate the height limit device. To benchmark the height limit estimation task, we build a large-scale dataset named "Disparity Height", where stereo images, pre-computed disparities and ground-truth height limit annotations are provided. We conducted extensive experiments on "Disparity Height" and the results show that SHLE achieves an average error below than 10cm though the car is 70m away from the devices. Our method also outperforms all compared baselines and achieves state-of-the-art performance. Code is available at https://github.com/Yang-Kaixing/SHLE.

Steering language generation towards objectives or away from undesired content has been a long-standing goal in utilizing language models (LM). Recent work has demonstrated reinforcement learning and weighted decoding as effective approaches to achieve a higher level of language control and quality with pros and cons. In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding. Specifically, we adopt the actor-critic framework to train an LM-steering critic from non-differentiable reward models. And similar to weighted decoding, our method freezes the language model and manipulates the output token distribution using called critic, improving training efficiency and stability. Evaluation of our method on three controlled generation tasks, namely topic control, sentiment control, and detoxification, shows that our approach generates more coherent and well-controlled texts than previous methods. In addition, CriticControl demonstrates superior generalization ability in zero-shot settings. Human evaluation studies also corroborate our findings.