情绪分析通常是许多注释器给出的主观标签的众群任务。尚未完全理解每个注释器的注释偏差如何使用最先进的方法正确建模。但是，精确且可靠地解决了注释偏见是了解注释器标记行为的关键，并成功解决有关注释任务的相应个人误解和不法行为。我们的贡献是精确神经端到端偏置建模和地面真理估计的解释和改进，这减少了对现有最先进的现有的不期望的不匹配。分类实验表明，在每个样品仅被一个单个注释器注释的情况下，它具有提高准确性。我们公开提供整个源代码，并释放包含10,000个句子的自己的域特定情绪数据集，讨论有机食品。这些蔓延从社交媒体上爬行，并由10名非专家注释器单独标记。

来自社交媒体的用户生成的内容是以多种语言产生的，在技术上挑战，将讨论的主题与不同文化和地区的一个域进行比较。它与全球化世界中的域名相关，例如市场研究，来自两个国家和市场的人可能对产品有不同的要求。我们提出了一种简单，现代化，有效的方法，用于建立一个具有情绪分析的单一主题模型，能够同时覆盖多种语言，基于预先接受的最先进的深度神经网络，用于自然语言理解。为了展示其可行性，我们将模型应用于报纸文章和用户评论，即有机食品和相关的消费行为。主题与语言相匹配。此外，我们获得了高比例的稳定和域名相关主题，主题与其各自的文本内容之间有意义的关系，以及社交媒体文档的可解释表示。营销可能会从我们的方法中受益，因为它提供了从全球各地的不同市场地区解决特定客户兴趣的易于使用手段。为了再现性，我们提供了我们研究的代码，数据和结果。

Estimating the 6D pose of objects is one of the major fields in 3D computer vision. Since the promising outcomes from instance-level pose estimation, the research trends are heading towards category-level pose estimation for more practical application scenarios. However, unlike well-established instance-level pose datasets, available category-level datasets lack annotation quality and provided pose quantity. We propose the new category level 6D pose dataset HouseCat6D featuring 1) Multi-modality of Polarimetric RGB+P and Depth, 2) Highly diverse 194 objects of 10 household object categories including 2 photometrically challenging categories, 3) High-quality pose annotation with an error range of only 1.35 mm to 1.74 mm, 4) 41 large scale scenes with extensive viewpoint coverage, 5) Checkerboard-free environment throughout the entire scene. We also provide benchmark results of state-of-the-art category-level pose estimation networks.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

We introduce M-VADER: a diffusion model (DM) for image generation where the output can be specified using arbitrary combinations of images and text. We show how M-VADER enables the generation of images specified using combinations of image and text, and combinations of multiple images. Previously, a number of successful DM image generation algorithms have been introduced that make it possible to specify the output image using a text prompt. Inspired by the success of those models, and led by the notion that language was already developed to describe the elements of visual contexts that humans find most important, we introduce an embedding model closely related to a vision-language model. Specifically, we introduce the embedding model S-MAGMA: a 13 billion parameter multimodal decoder combining components from an autoregressive vision-language model MAGMA and biases finetuned for semantic search.

Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static and homogeneous across individuals, so that aligning to a a single "generic" user will confer more general alignment. Here, we embrace the heterogeneity of human preferences to consider a different challenge: how might a machine help people with diverse views find agreement? We fine-tune a 70 billion parameter LLM to generate statements that maximize the expected approval for a group of people with potentially diverse opinions. Human participants provide written opinions on thousands of questions touching on moral and political issues (e.g., "should we raise taxes on the rich?"), and rate the LLM's generated candidate consensus statements for agreement and quality. A reward model is then trained to predict individual preferences, enabling it to quantify and rank consensus statements in terms of their appeal to the overall group, defined according to different aggregation (social welfare) functions. The model produces consensus statements that are preferred by human users over those from prompted LLMs (>70%) and significantly outperforms a tight fine-tuned baseline that lacks the final ranking step. Further, our best model's consensus statements are preferred over the best human-generated opinions (>65%). We find that when we silently constructed consensus statements from only a subset of group members, those who were excluded were more likely to dissent, revealing the sensitivity of the consensus to individual contributions. These results highlight the potential to use LLMs to help groups of humans align their values with one another.

在过去几年中，水下车辆操纵器系统（UVMS）变得越来越小，越来越小，在计划和控制系统时，考虑操纵器和车辆之间的耦合力变得越来越重要。但是，处理这些力的典型方法需要媒介物的精确流体动力模型，并在操纵器上使用低级扭矩控制，这两者在现场都很少见。因此，许多UVMS控制方法都是基于运动学的，无法固有地解释这些效果。我们的工作通过训练模拟UVMS数据上的复发性神经网络来弥合运动学控制与动态之间的差距，以根据系统以前的状态预测将来车辆的音高。运动学计划者和控制者可以使用此指标来合并动态知识，而无需计算昂贵的模型，从而提高了他们执行水下操纵任务的能力。

从具有高隐私要求的领域（例如医疗干预空间）获得的真实数据较低，并且收购在法律上很复杂。因此，这项工作提供了一种以医疗服装为例为医疗环境创建合成数据集的方法。目的是缩小合成数据和真实数据之间的现实差距。为此，使用虚幻的引擎插件或Unity比较了3D扫描服装和设计服装的方法。此外，还使用了绿屏和目标域数据集的混合现实数据集。我们的实验表明，设计服装的结构性域随机化以及混合现实数据提供了基线，可在临床目标域的测试数据集上实现72.0％的地图。当使用15％可用的目标域列车数据时，针对100％（660张图像）目标域列车数据的差距几乎可以关闭80.05％的地图（81.95％地图）。最后，我们表明，当使用100％目标域训练数据时，精度可以提高到83.35％的地图。

注释滥用语言很昂贵，在逻辑上复杂，并造成了心理伤害的风险。但是，大多数机器学习研究都优先提高有效性（即F1或精度得分），而不是数据效率（即，最小化注释的数据量）。在本文中，我们在两个数据集上使用模拟实验，以不同比例的滥用，以证明基于变形金刚的主动学习是一种有前途的方法，可以实质上提高效率，同时仍然保持高效，尤其是当虐待内容是数据集中较小比例的情况下。这种方法需要大量的标记数据，以达到与完整数据集培训相等的性能。

来自不同摄像头设备的光学相干断层扫描（OCT）成像会导致挑战域的变化，并可能导致机器学习模型的精度严重下降。在这项工作中，我们引入了基于单数值分解（SVDNA）的最小噪声适应方法，以克服视网膜OCT成像中三个不同设备制造商的目标域之间的域间隙。我们的方法利用噪声结构的差异成功地弥合了不同OCT设备之间的域间隙，并将样式从未标记的目标域图像转移到可用手动注释的源图像。我们演示了该方法尽管简单，但如何比较甚至胜过最先进的无监督域适应方法，用于在公共OCT数据集中进行语义细分。 SVDNA可以将仅几行代码集成到任何网络的增强管道中，这些网络与许多最新的域适应方法形成鲜明对比，这些方法通常需要更改基础模型体系结构或训练单独的样式转移模型。 SVDNA的完整代码实现可在https://github.com/valentinkoch/svdna上获得。