图形匹配优化问题是计算机视觉中许多任务的重要组成部分，例如在通信中带来两个可变形对象。自然，在过去的几十年中，已经提出了广泛的适用算法。由于尚未开发出通用的标准基准，因此由于对不同的问题实例的评估和标准使结果无与伦比，因此通常很难验证其绩效主张。为了解决这些缺点，我们提出了匹配算法的比较研究。我们创建了一个统一的基准测试标准，在其中收集和分类了一组现有和公开可用的计算机视觉图形匹配问题，以通用格式。同时，我们收集和分类图形匹配算法的最流行的开源实现。它们的性能以与比较优化算法的最佳实践相符的方式进行评估。该研究旨在可再现和扩展，以作为未来的宝贵资源。我们的研究提供了三个值得注意的见解：1。）流行问题实例在少于1秒的时间内完全可以解决，因此不足以进行将来的经​​验评估； 2.）最受欢迎的基线方法高于最佳可用方法； 3.）尽管该问题存在NP硬度，但即使对于具有超过500个顶点的图形，也可以在几秒钟内求解来自视力应用程序的实例。

我们提出了神经头头像，这是一种新型神经表示，其明确地模拟了可动画的人体化身的表面几何形状和外观，可用于在依赖数字人类的电影或游戏行业中的AR / VR或其他应用中的电话会议。我们的代表可以从单眼RGB肖像视频中学到，该视频具有一系列不同的表达和视图。具体地，我们提出了一种混合表示，其由面部的粗糙形状和表达式和两个前馈网络组成的混合表示，以及预测底层网格的顶点偏移以及视图和表达依赖性纹理。我们证明，该表示能够准确地外推到看不见的姿势和观点，并在提供尖锐的纹理细节的同时产生自然表达。与先前的磁头头像上的作品相比，我们的方法提供了与标准图形管道兼容的完整人体头（包括头发）的分解形状和外观模型。此外，就重建质量和新型观看合成而定量和定性地优于现有技术的当前状态。

We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation.

Neural networks are increasingly applied in safety critical domains, their verification thus is gaining importance. A large class of recent algorithms for proving input-output relations of feed-forward neural networks are based on linear relaxations and symbolic interval propagation. However, due to variable dependencies, the approximations deteriorate with increasing depth of the network. In this paper we present DPNeurifyFV, a novel branch-and-bound solver for ReLU networks with low dimensional input-space that is based on symbolic interval propagation with fresh variables and input-splitting. A new heuristic for choosing the fresh variables allows to ameliorate the dependency problem, while our novel splitting heuristic, in combination with several other improvements, speeds up the branch-and-bound procedure. We evaluate our approach on the airborne collision avoidance networks ACAS Xu and demonstrate runtime improvements compared to state-of-the-art tools.

Insects as pollinators play a key role in ecosystem management and world food production. However, insect populations are declining, calling for a necessary global demand of insect monitoring. Existing methods analyze video or time-lapse images of insects in nature, but the analysis is challenging since insects are small objects in complex and dynamic scenes of natural vegetation. The current paper provides a dataset of primary honeybees visiting three different plant species during two months of summer-period. The dataset consists of more than 700,000 time-lapse images from multiple cameras, including more than 100,000 annotated images. The paper presents a new method pipeline for detecting insects in time-lapse RGB-images. The pipeline consists of a two-step process. Firstly, the time-lapse RGB-images are preprocessed to enhance insects in the images. We propose a new prepossessing enhancement method: Motion-Informed-enhancement. The technique uses motion and colors to enhance insects in images. The enhanced images are subsequently fed into a Convolutional Neural network (CNN) object detector. Motion-Informed-enhancement improves the deep learning object detectors You Only Look Once (YOLO) and Faster Region-based Convolutional Neural Networks (Faster R-CNN). Using Motion-Informed-enhancement the YOLO-detector improves average micro F1-score from 0.49 to 0.71, and the Faster R-CNN-detector improves average micro F1-score from 0.32 to 0.56 on the our dataset. Our datasets are published on: https://vision.eng.au.dk/mie/

Reliable application of machine learning-based decision systems in the wild is one of the major challenges currently investigated by the field. A large portion of established approaches aims to detect erroneous predictions by means of assigning confidence scores. This confidence may be obtained by either quantifying the model's predictive uncertainty, learning explicit scoring functions, or assessing whether the input is in line with the training distribution. Curiously, while these approaches all state to address the same eventual goal of detecting failures of a classifier upon real-life application, they currently constitute largely separated research fields with individual evaluation protocols, which either exclude a substantial part of relevant methods or ignore large parts of relevant failure sources. In this work, we systematically reveal current pitfalls caused by these inconsistencies and derive requirements for a holistic and realistic evaluation of failure detection. To demonstrate the relevance of this unified perspective, we present a large-scale empirical study for the first time enabling benchmarking confidence scoring functions w.r.t all relevant methods and failure sources. The revelation of a simple softmax response baseline as the overall best performing method underlines the drastic shortcomings of current evaluation in the abundance of publicized research on confidence scoring. Code and trained models are at https://github.com/IML-DKFZ/fd-shifts.

Pre-trained protein language models have demonstrated significant applicability in different protein engineering task. A general usage of these pre-trained transformer models latent representation is to use a mean pool across residue positions to reduce the feature dimensions to further downstream tasks such as predicting bio-physics properties or other functional behaviours. In this paper we provide a two-fold contribution to machine learning (ML) driven drug design. Firstly, we demonstrate the power of sparsity by promoting penalization of pre-trained transformer models to secure more robust and accurate melting temperature (Tm) prediction of single-chain variable fragments with a mean absolute error of 0.23C. Secondly, we demonstrate the power of framing our prediction problem in a probabilistic framework. Specifically, we advocate for the need of adopting probabilistic frameworks especially in the context of ML driven drug design.

传统上，无监督的情感分析是通过计算存储在情感词典中的文本中的这些词，然后根据注册正面和否定词的比例分配标签的文字来执行的。尽管这些“计数”方法被认为是有益的，因为它们确定性地对文本进行评分，但当分析的文本简短或词汇与词典认为默认值的情况不同时，它们的分类率降低。本文提出的称为LEX2SENT的模型是一种无监督的情感分析方法，用于改善情感词典方法的分类。为此，对DOC2VEC模型进行了训练，以确定嵌入文档嵌入与情感词典正面和负部分的嵌入之间的距离。然后对这些距离进行评估，以在重新采样文档上多次执行DOC2VEC，并进行平均以执行分类任务。对于本文考虑的三个基准数据集，拟议的LEX2SENT优于每个评估的词典，包括Vader等最先进的词典或分类率的意见词典。

如何将新兴和全面的技术（例如AI）整合到我们社会的结构和运营中是当代政治，科学和公众辩论的问题。它从不同学科中产生了大量的国际学术文献。本文分析了有关人工智能调节（AI）的学术辩论。该系统审查包括在2016年1月1日至2020年12月31日之间发表的73份同行评审期刊文章样本。分析集中于社会风险和危害，监管责任问题以及可能基于风险的政策框架在内和基于原则的方法。主要利益是拟议的监管方法和工具。提出了各种形式的干预措施，例如禁令，批准，标准设定和披露。对所包括论文的评估​​表明该领域的复杂性，这表明其早产和剩余的缺乏清晰度。通过对学术辩论进行结构性分析，我们在经验和概念上均可更好地理解AI和监管的联系以及基本规范性决策。科学建议与拟议的欧洲AI调节的比较说明了调节的特定方法，其优势和缺点。

扩散模型是一类生成模型，与其他生成模型相比，在自然图像数据集训练时，在创建逼真的图像时表现出了出色的性能。我们引入了Dispr，这是一个基于扩散的模型，用于解决从二维（2D）单细胞显微镜图像预测三维（3D）细胞形状的反问题。使用2D显微镜图像作为先验，因此可以根据预测现实的3D形状重建条件。为了在基于功能的单细胞分类任务中展示DIPPR作为数据增强工具的适用性，我们从分组为六个高度不平衡类的单元中提取形态特征。将DISPR预测的功能添加到三个少数类别，将宏F1分数从$ f1_ \ text {macro} = 55.2 \ pm 4.6 \％$ to $ f1_ \％$ to $ f1_ \ text {macro} = 72.2 \ pm 4.9 \％$。由于我们的方法是在这种情况下第一个采用基于扩散的模型的方法，因此我们证明了扩散模型可以应用于3D中的反问题，并且他们学会了从2D显微镜图像中重建具有现实的形态特征的3D形状。