In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.

This paper presents a new hierarchical vision Transformer for image style transfer, called Strips Window Attention Transformer (S2WAT), which serves as an encoder of encoder-transfer-decoder architecture. With hierarchical features, S2WAT can leverage proven techniques in other fields of computer vision, such as feature pyramid networks (FPN) or U-Net, to image style transfer in future works. However, the existing window-based Transformers will cause a problem that the stylized images will be grid-like when introduced into image style transfer directly. To solve this problem, we propose S2WAT whose representation is computed with Strips Window Attention (SpW Attention). The SpW Attention can integrate both local information and long-range dependencies in horizontal and vertical directions by a novel feature fusion scheme named Attn Merge. Qualitative and quantitative experiments demonstrate that S2WAT achieves comparable performance to state-of-the-art CNN-based, Flow-based, and Transformer-based approaches. The code and models are available at https://github.com/AlienZhang1996/S2WAT.

过渡到成年是许多家庭的重要生活阶段。先前的研究表明，具有智力或发展的年轻人（IDD）比同龄人面临的挑战更多。这项研究是为了探索如何使用自然语言处理（NLP）方法，尤其是无监督的机器学习，以帮助心理学家分析情绪和情感，并使用主题建模来确定年轻人IDD及其家人所拥有的常见问题和挑战。此外，将结果与从没有IDD的年轻人那里获得的结果进行了比较。研究结果表明，NLP方法对于心理学家分析情绪，进行跨案例分析并从对话数据中汇总关键主题非常有用。我们的Python代码可在https://github.com/mlaricheva/emotion_topic_modeling上找到。

会话数据在心理学中至关重要，因为它可以帮助研究人员了解个人的认知过程，情感和行为。话语标签是分析此类数据的常见策略。 NLP算法的开发使研究人员可以自动化此任务。但是，心理对话数据给NLP研究人员带来了一些挑战，包括多标签分类，大量类别和有限的可用数据。这项研究探讨了NLP方法生成的自动标签如何与人类在成年过渡的对话的背景下与人类标签相媲美。我们提出了应对心理学研究中提出的三个共同挑战的策略。我们的发现表明，具有领域适应性的深度学习方法（Roberta-Con）优于所有其他机器学习方法。我们提出的分层标签系统被证明可帮助研究人员战略性地分析对话数据。我们的Python代码和NLP模型可在https://github.com/mlaricheva/automated_labeling上获得。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

2D-to-3D reconstruction is an ill-posed problem, yet humans are good at solving this problem due to their prior knowledge of the 3D world developed over years. Driven by this observation, we propose NeRDi, a single-view NeRF synthesis framework with general image priors from 2D diffusion models. Formulating single-view reconstruction as an image-conditioned 3D generation problem, we optimize the NeRF representations by minimizing a diffusion loss on its arbitrary view renderings with a pretrained image diffusion model under the input-view constraint. We leverage off-the-shelf vision-language models and introduce a two-section language guidance as conditioning inputs to the diffusion model. This is essentially helpful for improving multiview content coherence as it narrows down the general image prior conditioned on the semantic and visual features of the single-view input image. Additionally, we introduce a geometric loss based on estimated depth maps to regularize the underlying 3D geometry of the NeRF. Experimental results on the DTU MVS dataset show that our method can synthesize novel views with higher quality even compared to existing methods trained on this dataset. We also demonstrate our generalizability in zero-shot NeRF synthesis for in-the-wild images.

Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision. We propose OpenScene, an alternative approach where a model predicts dense features for 3D scene points that are co-embedded with text and image pixels in CLIP feature space. This zero-shot approach enables task-agnostic training and open-vocabulary queries. For example, to perform SOTA zero-shot 3D semantic segmentation it first infers CLIP features for every 3D point and later classifies them based on similarities to embeddings of arbitrary class labels. More interestingly, it enables a suite of open-vocabulary scene understanding applications that have never been done before. For example, it allows a user to enter an arbitrary text query and then see a heat map indicating which parts of a scene match. Our approach is effective at identifying objects, materials, affordances, activities, and room types in complex 3D scenes, all using a single model trained without any labeled 3D data.

近年来，由于其表达力和灵活性，神经隐式表示在3D重建中获得了普及。然而，神经隐式表示的隐式性质导致缓慢的推理时间并且需要仔细初始化。在本文中，我们重新审视经典且无处不在的点云表示，并使用泊松表面重建（PSR）的可分辨率配方引入可分化的点对网格层，其允许给予定向的GPU加速的指示灯的快速解决方案点云。可微分的PSR层允许我们通过隐式指示器字段有效地和分散地桥接与3D网格的显式3D点表示，从而实现诸如倒角距离的表面重建度量的端到端优化。因此，点和网格之间的这种二元性允许我们以面向点云表示形状，这是显式，轻量级和富有表现力的。与神经内隐式表示相比，我们的形状 - 点（SAP）模型更具可解释，轻量级，并通过一个级别加速推理时间。与其他显式表示相比，如点，补丁和网格，SA​​P产生拓扑无关的水密歧管表面。我们展示了SAP对无知点云和基于学习的重建的表面重建任务的有效性。

作为对话系统的基本组成部分，响应选择旨在挑选候选人之间的最佳反应，以继续对话。在现有研究中，这项任务通常被视为二进制分类问题，其中每个候选人分别排名以获取适当性。为了提高其性能，我们将此任务重构为一个多项选择问题，允许在一次性推断中进行最佳选择。这个新的视图激励我们提出一个名为全景 - 编码器的架构（我们的工作将是再现性和未来研究的开放来源。）具有新的候选人注意机制（CAM），这允许在响应之间进行情境方面的关注并导致良好-Gremator比较。此外，我们研究并纳入了一些已被证明有效改善响应选择的技术。三个基准测试的实验表明，我们的方法推动了最先进的，同时实现了大约3x的推理速度。

Training parts from ShapeNet. (b) t-SNE plot of part embeddings. (c) Reconstructing entire scenes with Local Implicit Grids Figure 1:We learn an embedding of parts from objects in ShapeNet [3] using a part autoencoder with an implicit decoder. We show that this representation of parts is generalizable across object categories, and easily scalable to large scenes. By localizing implicit functions in a grid, we are able to reconstruct entire scenes from points via optimization of the latent grid.