While recent advancements in artificial intelligence (AI) language models demonstrate cutting-edge performance when working with English texts, equivalent models do not exist in other languages or do not reach the same performance level. This undesired effect of AI advancements increases the gap between access to new technology from different populations across the world. This unsought bias mainly discriminates against individuals whose English skills are less developed, e.g., non-English speakers children. Following significant advancements in AI research in recent years, OpenAI has recently presented DALL-E: a powerful tool for creating images based on English text prompts. While DALL-E is a promising tool for many applications, its decreased performance when given input in a different language, limits its audience and deepens the gap between populations. An additional limitation of the current DALL-E model is that it only allows for the creation of a few images in response to a given input prompt, rather than a series of consecutive coherent frames that tell a story or describe a process that changes over time. Here, we present an easy-to-use automatic DALL-E storytelling framework that leverages the existing DALL-E model to enable fast and coherent visualizations of non-English songs and stories, pushing the limit of the one-step-at-a-time option DALL-E currently offers. We show that our framework is able to effectively visualize stories from non-English texts and portray the changes in the plot over time. It is also able to create a narrative and maintain interpretable changes in the description across frames. Additionally, our framework offers users the ability to specify constraints on the story elements, such as a specific location or context, and to maintain a consistent style throughout the visualization.

文本到图像合成的最新进展导致了较大的经过验证的变压器，具有出色的能力，可以从给定文本产生可视化。但是，这些模型不适合专门的任务，例如故事可视化，该任务要求代理商制作一系列图像，给定相应的字幕序列，形成叙述。此外，我们发现故事可视化任务无法适应新叙事中看不见的情节和角色的概括。因此，我们首先提出了故事延续的任务，其中生成的视觉故事是在源图像上进行的，从而可以更好地对具有新角色的叙述进行更好的概括。然后，我们使用特定于（a）顺序图像生成的任务特定模块和（b）从初始帧复制相关元素的任务特定模块来增强或“复古”文本对图像合成模型。然后，我们探讨了预训练模型的全模型芬太尼以及对参数适应的及时调整。我们在两个现有数据集（PororoSV和FlintStonessV）上评估了我们的方法storydall-e，并介绍了从视频吸引数据集收集的新数据集DIDEMOSV。我们还基于生成的对抗网络（GAN）开发了一个模型故事游戏，以进行故事的延续，并将其与StoryDall-E模型进行比较，以展示我们方法的优势。我们表明，我们的复古拟合方法优于基于GAN的模型，用于故事延续，并促进从源图像中复制视觉元素，从而改善了生成的视觉故事中的连续性。最后，我们的分析表明，经过审计的变压器努力理解包含几个角色的叙述。总体而言，我们的工作表明，可以验证的文本对图像合成模型可以适应复杂和低资源的任务，例如故事延续。

我们介绍了自回归文本到图像（Parti）模型的途径，该模型生成高保真的影像图像并支持涉及复杂组成和世界知识的内容丰富的合成。 Parti将文本对图像生成视为类似于机器翻译的序列到序列建模问题，图像令牌的序列是目标输出，而不是其他语言的文本令牌。这种策略自然可以利用大型语言模型的先前工作，通过扩展数据和模型尺寸，能力和性能的持续进展。我们的方法很简单：首先，Parti使用基于变压器的图像令牌VIT-VQGAN将图像编码为离散令牌的序列。其次，我们通过将编码器二次变压器模型缩放到20B参数来实现一致的质量改进，其新的最新零弹药FID得分为7.23，而MS-Coco的FIDED得分为3.22。我们对本地化叙述以及党的详细分析（P2），这是1600多个英语提示的新的整体基准，证明了Parti在各种类别和难度方面的有效性。我们还探索并突出了我们的模型的局限性，以定义和体现关注重点领域以进一步改进。有关高分辨率图像，请参见https://parti.research.google/。

We consider the problem of automatically generating stories in multiple languages. Compared to prior work in monolingual story generation, crosslingual story generation allows for more universal research on story planning. We propose to use Prompting Large Language Models with Plans to study which plan is optimal for story generation. We consider 4 types of plans and systematically analyse how the outputs differ for different planning strategies. The study demonstrates that formulating the plans as question-answer pairs leads to more coherent generated stories while the plan gives more control to the story creators.

We propose the Detailed Outline Control (DOC) framework for improving long-range plot coherence when automatically generating several-thousand-word-long stories. DOC consists of two complementary components: a detailed outliner and a detailed controller. The detailed outliner creates a more detailed, hierarchically structured outline, shifting creative burden from the main drafting procedure to the planning stage. The detailed controller ensures the more detailed outline is still respected during generation by controlling story passages to align with outline details. In human evaluations of automatically generated stories, DOC substantially outperforms a strong Re3 baseline (Yang et al., 2022) on plot coherence (22.5% absolute gain), outline relevance (28.2%), and interestingness (20.7%). Humans also judged DOC to be much more controllable in an interactive generation setting.

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 2nd International Workshop on Reading Music Systems, held in Delft on the 2nd of November 2019.

Large language models trained for code generation can be applied to speaking virtual worlds into existence (creating virtual worlds). In this work we show that prompt-based methods can both accelerate in-VR level editing, as well as can become part of gameplay rather than just part of game development. As an example, we present Codex VR Pong which shows non-deterministic game mechanics using generative processes to not only create static content but also non-trivial interactions between 3D objects. This demonstration naturally leads to an integral discussion on how one would evaluate and benchmark experiences created by generative models - as there are no qualitative or quantitative metrics that apply in these scenarios. We conclude by discussing impending challenges of AI-assisted co-creation in VR.

自然语言为图像编辑提供高度直观的界面。在本文中，我们基于自然语言描述与ROI掩模一起介绍用于在通用自然图像中执行局部（基于区域的）编辑的第一解决方案。我们通过利用并结合预先训练的语言图像模型（CLIP）来实现我们的目标，以使编辑朝向用户提供的文本提示，具有去噪扩散概率模型（DDPM）来产生自然的结果。为了使编辑区域与图像的不变部分无缝熔化，我们在噪声水平的进展下使用本地文本引导的扩散潜伏在空间上混合输入图像的声明版本。此外，我们表明向扩散过程增加增强，减轻了对抗性结果。我们与定性和定量的几个基线和相关方法进行比较，并表明我们的方法在整体现实主义方面优于这些解决方案，保留背景和匹配文本的能力。最后，我们显示了多个文本驱动的编辑应用程序，包括将新对象添加到图像，删除/替换/更改现有对象，背景替换和图像外推。

视频可访问性对于盲人和低愿景用户来说至关重要，以获得教育，就业和娱乐的公平参与。尽管有专业和业余服务和工具，但大多数人类生成的描述都很昂贵且耗时。此外，人生成的描述的速率不能匹配视频产生的速度。为了克服视频可访问性的越来越多的空白，我们开发了两个工具的混合系统到1）自动生成视频的描述，2）提供响应于视频上的用户查询的答案或附加描述。与26例盲和低视力下的混合方法研究结果表明，当两种工具在串联中使用时，我们的系统会显着提高用户理解和享受所选视频的理解和享受。此外，参与者报告说，在呈现自生物的描述与人类修订的自动化描述相关时，没有显着差异。我们的结果表明了对发达系统的热情及其承诺提供对视频的定制访问。我们讨论了当前工作的局限性，并为自动视频描述工具的未来发展提供了建议。

本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片（Sota）。它填补了空白，因为现有的调查文章在其范围内或被约会。我们包括两个重要方面，目前正在挖掘和建模社交媒体的重要性：动态和网络。社会动态对于了解影响影响或疾病的传播，友谊的形成，友谊的形成等，另一方面，可以捕获各种复杂关系，提供额外的洞察力和识别否则将不会被注意的重要模式。

手语是聋人和听力受损社区中使用的沟通语言的主要形式。在听力障碍和听力社区之间进行简单互相的沟通，建立一个能够将口语翻译成手语的强大系统，反之亦然是基本的。为此，标志语言识别和生产是制作这种双向系统的两个必要零件。手语识别和生产需要应对一些关键挑战。在这项调查中，我们审查了使用深度学习的手语制作（SLP）和相关领域的最近进展。为了有更现实的观点来签署语言，我们介绍了聋人文化，聋人中心，手语的心理视角，口语和手语之间的主要差异。此外，我们介绍了双向手语翻译系统的基本组成部分，讨论了该领域的主要挑战。此外，简要介绍了SLP中的骨干架构和方法，并提出了拟议的SLP分类物。最后，介绍了SLP和绩效评估的一般框架，也讨论了SLP最近的发展，优势和限制，评论可能的未来研究的可能线条。

从开放式域文本提示中生成和编辑图像是迄今为止需要昂贵且经过特殊训练的型号的一项挑战性的任务。我们为这两个任务展示了一种新颖的方法，该方法能够通过使用多模式编码器来指导图像世代，从而从具有显着语义复杂性的文本提示中产生高视觉质量的图像。我们在各种任务上说明了如何使用夹[37]引导VQGAN [11]产生的视觉质量输出比先前的较不灵活的方法，例如DALL-E [38]，Glide [33]和Open-Edit [24]，尽管没有接受培训的任务。我们的代码在公共存储库中可用。

Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that are more natural and better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more diverse and fluent text. However, due to the lower level of interpretability of deep neural networks, the controllability of these methods need to be guaranteed. To this end, controllable text generation using transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A diverse range of approaches have emerged in the recent 3-4 years, targeting different CTG tasks which may require different types of controlled constraints. In this paper, we present a systematic critical review on the common tasks, main approaches and evaluation methods in this area. Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey paper to summarize CTG techniques from the perspective of PLMs. We hope it can help researchers in related fields to quickly track the academic frontier, providing them with a landscape of the area and a roadmap for future research.

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 3rd International Workshop on Reading Music Systems, held in Alicante on the 23rd of July 2021.

Current image generation models struggle to reliably produce well-formed visual text. In this paper, we investigate a key contributing factor: popular text-to-image models lack character-level input features, making it much harder to predict a word's visual makeup as a series of glyphs. To quantify the extent of this effect, we conduct a series of controlled experiments comparing character-aware vs. character-blind text encoders. In the text-only domain, we find that character-aware models provide large gains on a novel spelling task (WikiSpell). Transferring these learnings onto the visual domain, we train a suite of image generation models, and show that character-aware variants outperform their character-blind counterparts across a range of novel text rendering tasks (our DrawText benchmark). Our models set a much higher state-of-the-art on visual spelling, with 30+ point accuracy gains over competitors on rare words, despite training on far fewer examples.

当前的语言模型可以产生高质量的文本。他们只是复制他们之前看到的文本，或者他们学习了普遍的语言抽象吗？要取笑这些可能性，我们介绍了乌鸦，这是一套评估生成文本的新颖性，专注于顺序结构（n-gram）和句法结构。我们将这些分析应用于四种神经语言模型（LSTM，变压器，变换器-XL和GPT-2）。对于本地结构 - 例如，单个依赖性 - 模型生成的文本比来自每个模型的测试集的人类生成文本的基线显着不那么新颖。对于大规模结构 - 例如，总句结构 - 模型生成的文本与人生成的基线一样新颖甚至更新颖，但模型仍然有时复制，在某些情况下，在训练集中重复超过1000字超过1,000字的通道。我们还表现了广泛的手动分析，表明GPT-2的新文本通常在形态学和语法中形成良好，但具有合理的语义问题（例如，是自相矛盾）。

We propose "factor matting", an alternative formulation of the video matting problem in terms of counterfactual video synthesis that is better suited for re-composition tasks. The goal of factor matting is to separate the contents of video into independent components, each visualizing a counterfactual version of the scene where contents of other components have been removed. We show that factor matting maps well to a more general Bayesian framing of the matting problem that accounts for complex conditional interactions between layers. Based on this observation, we present a method for solving the factor matting problem that produces useful decompositions even for video with complex cross-layer interactions like splashes, shadows, and reflections. Our method is trained per-video and requires neither pre-training on external large datasets, nor knowledge about the 3D structure of the scene. We conduct extensive experiments, and show that our method not only can disentangle scenes with complex interactions, but also outperforms top methods on existing tasks such as classical video matting and background subtraction. In addition, we demonstrate the benefits of our approach on a range of downstream tasks. Please refer to our project webpage for more details: https://factormatte.github.io

MetaVerse，巨大的虚拟物理网络空间，为艺术家带来了前所未有的机会，将我们的身体环境的每个角落与数字创造力混合。本文对计算艺术进行了全面的调查，其中七个关键主题与成权相关，描述了混合虚拟物理现实中的新颖艺术品。主题首先涵盖了MetaVerse的建筑元素，例如虚拟场景和字符，听觉，文本元素。接下来，已经反映了诸如沉浸式艺术，机器人艺术和其他用户以其他用户的方法提供了沉浸式艺术，机器人艺术和其他用户中心的若干非凡类型的新颖创作。最后，我们提出了几项研究议程：民主化的计算艺术，数字隐私和搬迁艺术家的安全性，为数字艺术品，技术挑战等等的所有权认可。该调查还担任艺术家和搬迁技术人员的介绍材料，以开始在超现实主义网络空间领域创造。

本文提供了当前视频内容提取工具的比较，重点是比较基于任务的机器学习服务。在过去十年中，视频智能（VIDINT）数据已成为关键情报来源。基于AI的分析和自动化工具从视频中提取和构造内容的需求已迅速成为需要大规模搜索，分析和利用视频的组织的优先事项。随着机器学习技术的快速增长，机器转录，机器翻译，主题标签和对象识别任务的成熟度以指数级的速度提高，随着新应用程序的发展，速度和准确性的性能记录破坏了。本文的每个部分审查并根据与机器学习技术从视频中提取信息相关的任务进行了比较产品，软件资源和视频分析功能。