深层生成模型有可能从根本上改变我们创建高保真数字内容的方式，但通常很难控制。提示生成模型是一个有希望的最新发展，原则上，最终用户可以创造性地利用零击和几乎没有学习的学习来将新任务分配给AI Ad-Hoc，只需将其写下即可。但是，对于大多数最终用户而言，编写有效提示目前主要是试验和错误过程。为了解决这个问题，我们讨论了使用促使人类互动的新范式的交互式创意应用程序的关键机会和挑战。根据我们的分析，我们为支持提示的用户界面提出了四个设计目标。我们用混凝土UI设计草图说明了这些内容，重点是创意写作的用例。HCI和AI的研究社区可以将这些作为起点，以开发足够的用户界面，以供能够零和少数学习的模型。

我们提出了一个文本编辑器，以帮助用户计划，结构并反思其写作过程。它使用自动文本摘要提供了不断更新的段落摘要作为边缘注释。摘要级别范围从全文到选定的（中央）句子，一直到关键字的集合。为了了解用户在写作过程中如何与该系统进行交互，我们进行了两项用户研究（n = 4和n = 8），人们在其中写了有关给定主题和文章的分析文章。作为关键发现，这些摘要使用户对他们的写作有了外部视角，并帮助他们修改了草稿段落的内容和范围。人们进一步使用该工具快速获得文本概述，并制定了整合自动摘要中见解的策略。从更广泛的角度来看，这项工作探索并突出了为作家设计AI工具的价值，其自然语言处理（NLP）功能超出了直接文本生成和更正。

神经语言模型有可能支持人类写作。但是，关于其整合和对写作和产出的影响仍然存在问题。为了解决这个问题，我们设计并比较了两个用于写作的用户界面与移动设备上的AI，这些用户界面操纵主动性和控制级别：1）使用连续生成的文本编写，AI添加了逐字文字和用户转向。 2）编写建议，AI建议短语和用户从列表中选择。在监督的在线研究（n = 18）中，参与者使用了这些原型和无AI的基线。我们收集了触摸互动，关于灵感和作者的评分以及访谈数据。有了AI的建议，人们的写作不那么积极，但觉得他们是作者。连续生成的文本减少了这种感知的作者身份，但编辑行为增加了。在这两种设计中，AI都会增加文本长度，并被认为会影响措辞。我们的发现为UI设计决策对用户体验和共同创造系统的产出的影响增加了新的经验证据。

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

Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.

Keyword spotting (KWS) based on deep neural networks (DNNs) has achieved massive success in voice control scenarios. However, training of such DNN-based KWS systems often requires significant data and hardware resources. Manufacturers often entrust this process to a third-party platform. This makes the training process uncontrollable, where attackers can implant backdoors in the model by manipulating third-party training data. An effective backdoor attack can force the model to make specified judgments under certain conditions, i.e., triggers. In this paper, we design a backdoor attack scheme based on Voiceprint Selection and Voice Conversion, abbreviated as VSVC. Experimental results demonstrated that VSVC is feasible to achieve an average attack success rate close to 97% in four victim models when poisoning less than 1% of the training data.

Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose, a versatile framework to model plausible 3D human poses for various applications. At the core of GFPose is a time-dependent score network, which estimates the gradient on each body joint and progressively denoises the perturbed 3D human pose to match a given task specification. During the denoising process, GFPose implicitly incorporates pose priors in gradients and unifies various discriminative and generative tasks in an elegant framework. Despite the simplicity, GFPose demonstrates great potential in several downstream tasks. Our experiments empirically show that 1) as a multi-hypothesis pose estimator, GFPose outperforms existing SOTAs by 20% on Human3.6M dataset. 2) as a single-hypothesis pose estimator, GFPose achieves comparable results to deterministic SOTAs, even with a vanilla backbone. 3) GFPose is able to produce diverse and realistic samples in pose denoising, completion and generation tasks. Project page https://sites.google.com/view/gfpose/

Open-Domain Question Answering (ODQA) requires models to answer factoid questions with no context given. The common way for this task is to train models on a large-scale annotated dataset to retrieve related documents and generate answers based on these documents. In this paper, we show that the ODQA architecture can be dramatically simplified by treating Large Language Models (LLMs) as a knowledge corpus and propose a Self-Prompting framework for LLMs to perform ODQA so as to eliminate the need for training data and external knowledge corpus. Concretely, we firstly generate multiple pseudo QA pairs with background passages and one-sentence explanations for these QAs by prompting LLMs step by step and then leverage the generated QA pairs for in-context learning. Experimental results show our method surpasses previous state-of-the-art methods by +8.8 EM averagely on three widely-used ODQA datasets, and even achieves comparable performance with several retrieval-augmented fine-tuned models.

Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time interval between these two items. However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of \emph{preference drift}. In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-Reorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of variance of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths. Finally, we implement these improvements on a state-of-the-art model CoSeRec and validate our approach on four real datasets. The experimental results show that our approach reaches significantly better performance than the other 11 competing methods. Our implementation is available: https://github.com/KingGugu/TiCoSeRec.

For solving a broad class of nonconvex programming problems on an unbounded constraint set, we provide a self-adaptive step-size strategy that does not include line-search techniques and establishes the convergence of a generic approach under mild assumptions. Specifically, the objective function may not satisfy the convexity condition. Unlike descent line-search algorithms, it does not need a known Lipschitz constant to figure out how big the first step should be. The crucial feature of this process is the steady reduction of the step size until a certain condition is fulfilled. In particular, it can provide a new gradient projection approach to optimization problems with an unbounded constrained set. The correctness of the proposed method is verified by preliminary results from some computational examples. To demonstrate the effectiveness of the proposed technique for large-scale problems, we apply it to some experiments on machine learning, such as supervised feature selection, multi-variable logistic regressions and neural networks for classification.