大脑磁共振成像（MRI）扫描的自动分割和体积对于诊断帕金森氏病（PD）和帕金森氏症综合症（P-Plus）至关重要。为了提高诊断性能，我们在大脑分割中采用了深度学习（DL）模型，并将其性能与金标准的非DL方法进行了比较。我们收集了健康对照组（n = 105）和PD患者（n = 105），多个全身性萎缩（n = 132）和渐进性超核麻痹（n = 69）的大脑MRI扫描。 2020.使用金标准的非DL模型FreeSurfer（FS），我们对六个脑结构进行了分割：中脑，PON，CAUDATE，CAUDATE，PUTATATE，pALLIDUM和THIRD CNTRICLE，并将其视为DL模型的注释数据，代表性V -net和unet。计算了分化正常，PD和P-Plus病例的曲线下的骰子分数和面积。每位患者六个大脑结构的V-NET和UNETR的分割时间分别为3.48 +-0.17和48.14 +-0.97 s，比FS（15,735 +-1.07 s）快至少300倍。两种DL模型的骰子得分都足够高（> 0.85），它们的疾病分类AUC优于FS。为了分类正常与P-Plus和PD与多个全身性萎缩（小脑型）的分类，DL模型和FS显示出高于0.8的AUC。 DL显着减少了分析时间，而不会损害大脑分割和差异诊断的性能。我们的发现可能有助于在临床环境中采用DL脑MRI分割并提高大脑研究。

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.

While 3D GANs have recently demonstrated the high-quality synthesis of multi-view consistent images and 3D shapes, they are mainly restricted to photo-realistic human portraits. This paper aims to extend 3D GANs to a different, but meaningful visual form: artistic portrait drawings. However, extending existing 3D GANs to drawings is challenging due to the inevitable geometric ambiguity present in drawings. To tackle this, we present Dr.3D, a novel adaptation approach that adapts an existing 3D GAN to artistic drawings. Dr.3D is equipped with three novel components to handle the geometric ambiguity: a deformation-aware 3D synthesis network, an alternating adaptation of pose estimation and image synthesis, and geometric priors. Experiments show that our approach can successfully adapt 3D GANs to drawings and enable multi-view consistent semantic editing of drawings.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

特征相似性匹配将参考框架的信息传输到查询框架，是半监视视频对象分割中的关键组件。如果采用了汇总匹配，则背景干扰器很容易出现并降低性能。徒匹配机制试图通过限制要传输到查询框架的信息的量来防止这种情况，但是有两个局限性：1）由于在测试时转换为两种匹配，因此无法完全利用过滤匹配的匹配； 2）搜索最佳超参数需要测试时间手动调整。为了在确保可靠的信息传输的同时克服这些局限性，我们引入了均衡的匹配机制。为了防止参考框架信息过于引用，通过简单地将SoftMax操作与查询一起应用SoftMax操作，对查询框架的潜在贡献得到了均等。在公共基准数据集上，我们提出的方法与最先进的方法达到了可比的性能。

多年来，为各种对象检测任务开发了数据集。海事域中的对象检测对于船舶的安全和导航至关重要。但是，在海事域中，仍然缺乏公开可用的大规模数据集。为了克服这一挑战，我们提出了Kolomverse，这是一个开放的大型图像数据集，可在Kriso（韩国研究所和海洋工程研究所）的海事域中进行物体检测。我们收集了从韩国21个领土水域捕获的5,845小时的视频数据。通过精心设计的数据质量评估过程，我们从视频数据中收集了大约2,151,470 4K分辨率的图像。该数据集考虑了各种环境：天气，时间，照明，遮挡，观点，背景，风速和可见性。 Kolomverse由五个类（船，浮标，渔网浮标，灯塔和风电场）组成，用于海上对象检测。该数据集的图像为3840美元$ \ times $ 2160像素，据我们所知，它是迄今为止最大的公开数据集，用于海上域中的对象检测。我们进行了对象检测实验，并在几个预训练的最先进的架构上评估了我们的数据集，以显示我们数据集的有效性和实用性。该数据集可在：\ url {https://github.com/maritimedataset/kolomverse}中获得。

The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, `Hanja', and were translated into Korean from 1968 to 1993. The resulting translation was however too literal and contained many archaic Korean words; thus, a new expert translation effort began in 2012. Since then, the records of only one king have been completed in a decade. In parallel, expert translators are working on English translation, also at a slow pace and produced only one king's records in English so far. Thus, we propose H2KE, a neural machine translation model, that translates historical documents in Hanja to more easily understandable Korean and to English. Built on top of multilingual neural machine translation, H2KE learns to translate a historical document written in Hanja, from both a full dataset of outdated Korean translation and a small dataset of more recently translated contemporary Korean and English. We compare our method against two baselines: a recent model that simultaneously learns to restore and translate Hanja historical document and a Transformer based model trained only on newly translated corpora. The experiments reveal that our method significantly outperforms the baselines in terms of BLEU scores for both contemporary Korean and English translations. We further conduct extensive human evaluation which shows that our translation is preferred over the original expert translations by both experts and non-expert Korean speakers.

当我们开发可穿戴辅助设备时，舒适和支持是需要考虑的两个主要问题。在传统的设计方法中，穿着者的联合运动自由度往往是超薄的。因此，佩戴者的运动变得克制，并且在意外下降时可能会发生骨/韧带损伤。为了减轻这些问题，这封信提出了一种由人类脊柱结构和功能的新型联合联系机制。所提出的脊柱状关节连杆机构的关键特征是通过柔性合成纤维线连接到半球形块，使得它们的级联刚度可以根据拉伸力调节。这一功能具有设计可穿戴辅助设备的巨大壮大，可通过调节倾斜刚度来支持老年人的坐足式动作或增加脊柱运动。此外，连接的半球形块使得穿着者能够将他/她的关节移动到全部自由度，这反过来增加了穿着者的流动性并阻止联合未对准。使用试验台和试验机的实验结果证实了脊柱状的接头连杆机构可以用作设计可穿戴辅助装置的关键部件，以便更好地移动和安全性。

由于锥形光束计算机断层扫描（CBCT）图像的三维（3D）单个齿的准确和自动分割是一个具有挑战性的问题，因为难以将个体齿与相邻齿和周围的肺泡骨分开。因此，本文提出了一种从牙科CBCT图像识别和分割3D个体齿的全自动方法。所提出的方法通过开发基于深度学习的分层多步模型来解决上述难度。首先，它自动生成上下钳口全景图像，以克服由高维数据和与有限训练数据集相关的维度的诅咒引起的计算复杂度。然后使用所获得的2D全景图像来识别2D单独的牙齿并捕获3D个体齿的兴趣和紧密区域（ROIS）。最后，使用松动和紧密的ROI实现了精确的3D个体齿分割。实验结果表明，牙齿识别的牙齿识别的F1分数为93.35％，对于各个3D齿分割，骰子相似度系数为94.79％。结果表明，该方法为数字牙科提供了有效的临床和实用框架。

在当代社会中，监视异常检测，即在监视视频中发现异常事件，例如犯罪或事故，是一项关键任务。由于异常发生很少发生，大多数培训数据包括没有标记的视频，没有异常事件，这使得任务具有挑战性。大多数现有方法使用自动编码器（AE）学习重建普通视频；然后，他们根据未能重建异常场景的出现来检测异常。但是，由于异常是通过外观和运动来区分的，因此许多先前的方法使用预训练的光流模型明确分开了外观和运动信息，例如。这种明确的分离限制了两种类型的信息之间的相互表示功能。相比之下，我们提出了一个隐式的两路AE（ITAE），其中两个编码器隐含模型外观和运动特征以及一个将它们组合在一起以学习正常视频模式的结构。对于正常场景的复杂分布，我们建议通过归一化流量（NF）的生成模型对ITAE特征的正常密度估计，以学习可拖动的可能性，并使用无法分布的检测来识别异常。 NF模型通过隐式学习的功能通过学习正常性来增强ITAE性能。最后，我们在六个基准测试中演示了ITAE及其特征分布建模的有效性，包括在现实世界中包含各种异常的数据库。