Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alternative to approaching general-purpose AI, existing generalist models are still at an early stage, where modality and task coverage is limited. To empower multi-modal task-scaling and speed up this line of research, we release a generalist model learning system, OFASys, built on top of a declarative task interface named multi-modal instruction. At the core of OFASys is the idea of decoupling multi-modal task representations from the underlying model implementations. In OFASys, a task involving multiple modalities can be defined declaratively even with just a single line of code. The system automatically generates task plans from such instructions for training and inference. It also facilitates multi-task training for diverse multi-modal workloads. As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data. The single OFA+ model achieves 95% performance in average with only 16% parameters of 15 task-finetuned models, showcasing the performance reliability of multi-modal task-scaling provided by OFASys. Available at https://github.com/OFA-Sys/OFASys

Generative modeling of human motion has broad applications in computer animation, virtual reality, and robotics. Conventional approaches develop separate models for different motion synthesis tasks, and typically use a model of a small size to avoid overfitting the scarce data available in each setting. It remains an open question whether developing a single unified model is feasible, which may 1) benefit the acquirement of novel skills by combining skills learned from multiple tasks, and 2) help in increasing the model capacity without overfitting by combining multiple data sources. Unification is challenging because 1) it involves diverse control signals as well as targets of varying granularity, and 2) motion datasets may use different skeletons and default poses. In this paper, we present MoFusion, a framework for unified motion synthesis. MoFusion employs a Transformer backbone to ease the inclusion of diverse control signals via cross attention, and pretrains the backbone as a diffusion model to support multi-granularity synthesis ranging from motion completion of a body part to whole-body motion generation. It uses a learnable adapter to accommodate the differences between the default skeletons used by the pretraining and the fine-tuning data. Empirical results show that pretraining is vital for scaling the model size without overfitting, and demonstrate MoFusion's potential in various tasks, e.g., text-to-motion, motion completion, and zero-shot mixing of multiple control signals. Project page: \url{https://ofa-sys.github.io/MoFusion/}.

在本文中，我们提出了用于滚动快门摄像机的概率连续时间视觉惯性频道（VIO）。连续的时轨迹公式自然促进异步高频IMU数据和运动延伸的滚动快门图像的融合。为了防止棘手的计算负载，提出的VIO是滑动窗口和基于密钥帧的。我们建议概率地将控制点边缘化，以保持滑动窗口中恒定的密钥帧数。此外，可以在我们的连续时间VIO中在线校准滚动快门相机的线曝光时间差（线延迟）。为了广泛检查我们的连续时间VIO的性能，对公共可用的WHU-RSVI，TUM-RSVI和Sensetime-RSVI Rolling快门数据集进行了实验。结果表明，提出的连续时间VIO显着优于现有的最新VIO方法。本文的代码库也将通过\ url {https://github.com/april-zju/ctrl-vio}开源。

少量样本压缩旨在将大冗余模型压缩成一个小型紧凑型，只有少量样品。如果我们的微调模型直接具有这些限制的样本，模型将容易受到过度装备，并且几乎没有学习。因此，先前的方法优化压缩模型逐层，并尝试使每个层具有与教师模型中的相应层相同的输出，这是麻烦的。在本文中，我们提出了一个名为mimicking的新框架，然后替换（mir），以实现几个样本压缩，这首先促使修剪模型输出与教师在倒数第二层中的相同功能，然后在倒数第二个之前替换教师的图层调整良好的紧凑型。与以前的层面重建方法不同，我们的MIR完全优化整个网络，这不仅简单而有效，而且还无人驾驶和一般。MIR优于以前的余量。代码即将推出。

受到深入学习的巨大成功通过云计算和边缘芯片的快速发展的影响，人工智能研究（AI）的研究已经转移到计算范例，即云计算和边缘计算。近年来，我们目睹了在云服务器上开发更高级的AI模型，以超越传统的深度学习模型，以造成模型创新（例如，变压器，净化家庭），训练数据爆炸和飙升的计算能力。但是，边缘计算，尤其是边缘和云协同计算，仍然在其初期阶段，因为由于资源受限的IOT场景，因此由于部署了非常有限的算法而导致其成功。在本调查中，我们对云和边缘AI进行系统审查。具体而言，我们是第一个设置云和边缘建模的协作学习机制，通过彻底的审查使能够实现这种机制的架构。我们还讨论了一些正在进行的先进EDGE AI主题的潜在和实践经验，包括预先训练模型，图形神经网络和加强学习。最后，我们讨论了这一领域的有希望的方向和挑战。

条件图像合成旨在根据文本描述，参考图像和图像块的形式创建图像，以保存的，以及它们的组合。在本文中，我们提出了一个新的两级架构M6-UFC，统一了任何数量的多模态控件。在M6-UFC中，各种控制信号和合成图像都均匀地表示为由变压器处理的离散令牌序列。与现有的两级自回归方式不同，如Dall-E和VQGAN，M6-UFC在第二阶段采用非自动发作生成（NAR），以增强合成图像的整体一致性，以支持保留指定的图像块，以及提高合成速度。此外，我们设计了一种逐步算法，其迭代地改善了非自动产生的图像，其中包括用于评估符合控制的符合和评估合成图像的保真度的两个估计器的帮助。在新收集的大型服装数据集M2C时装和面部数据集多模态Celeba-HQ上进行了广泛的实验验证了M6-UFC可以合成符合灵活的多模态控制的高保真图像。

Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption cannot always be satisfied, as real-world graphs are always incomplete, noisy, or redundant. How to reveal the inherent graph structure in a unified way remains under-explored. We proposed PRI-GSL, a Graph Structure Learning framework guided by the Principle of Relevant Information, providing a simple and unified framework for identifying the self-organization and revealing the hidden structure. PRI-GSL learns a structure that contains the most relevant yet least redundant information quantified by von Neumann entropy and Quantum Jensen-Shannon divergence. PRI-GSL incorporates the evolution of quantum continuous walk with graph wavelets to encode node structural roles, showing in which way the nodes interplay and self-organize with the graph structure. Extensive experiments demonstrate the superior effectiveness and robustness of PRI-GSL.

Federated learning achieves joint training of deep models by connecting decentralized data sources, which can significantly mitigate the risk of privacy leakage. However, in a more general case, the distributions of labels among clients are different, called ``label distribution skew''. Directly applying conventional federated learning without consideration of label distribution skew issue significantly hurts the performance of the global model. To this end, we propose a novel federated learning method, named FedMGD, to alleviate the performance degradation caused by the label distribution skew issue. It introduces a global Generative Adversarial Network to model the global data distribution without access to local datasets, so the global model can be trained using the global information of data distribution without privacy leakage. The experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art on several public benchmarks. Code is available at \url{https://github.com/Sheng-T/FedMGD}.

Text-driven person image generation is an emerging and challenging task in cross-modality image generation. Controllable person image generation promotes a wide range of applications such as digital human interaction and virtual try-on. However, previous methods mostly employ single-modality information as the prior condition (e.g. pose-guided person image generation), or utilize the preset words for text-driven human synthesis. Introducing a sentence composed of free words with an editable semantic pose map to describe person appearance is a more user-friendly way. In this paper, we propose HumanDiffusion, a coarse-to-fine alignment diffusion framework, for text-driven person image generation. Specifically, two collaborative modules are proposed, the Stylized Memory Retrieval (SMR) module for fine-grained feature distillation in data processing and the Multi-scale Cross-modality Alignment (MCA) module for coarse-to-fine feature alignment in diffusion. These two modules guarantee the alignment quality of the text and image, from image-level to feature-level, from low-resolution to high-resolution. As a result, HumanDiffusion realizes open-vocabulary person image generation with desired semantic poses. Extensive experiments conducted on DeepFashion demonstrate the superiority of our method compared with previous approaches. Moreover, better results could be obtained for complicated person images with various details and uncommon poses.

In this work, we focus on the problem of safe policy transfer in reinforcement learning: we seek to leverage existing policies when learning a new task with specified constraints. This problem is important for safety-critical applications where interactions are costly and unconstrained policies can lead to undesirable or dangerous outcomes, e.g., with physical robots that interact with humans. We propose a Constrained Markov Decision Process (CMDP) formulation that simultaneously enables the transfer of policies and adherence to safety constraints. Our formulation cleanly separates task goals from safety considerations and permits the specification of a wide variety of constraints. Our approach relies on a novel extension of generalized policy improvement to constrained settings via a Lagrangian formulation. We devise a dual optimization algorithm that estimates the optimal dual variable of a target task, thus enabling safe transfer of policies derived from successor features learned on source tasks. Our experiments in simulated domains show that our approach is effective; it visits unsafe states less frequently and outperforms alternative state-of-the-art methods when taking safety constraints into account.