Images in visualization publications contain rich information, e.g., novel visualization designs and implicit design patterns of visualizations. A systematic collection of these images can contribute to the community in many aspects, such as literature analysis and automated tasks for visualization. In this paper, we build and make public a dataset, VisImages, which collects 12,267 images with captions from 1,397 papers in IEEE InfoVis and VAST. Built upon a comprehensive visualization taxonomy, the dataset includes 35,096 visualizations and their bounding boxes in the images.We demonstrate the usefulness of VisImages through three use cases: 1) investigating the use of visualizations in the publications with VisImages Explorer, 2) training and benchmarking models for visualization classification, and 3) localizing visualizations in the visual analytics systems automatically.
Is this chart lying to me? Automating the detection of misleading visualizations
Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, TU Darmstadt and National Research Center for Applied Cybersecurity ATHENE（普遍知识处理实验室（UKP实验室）、计算机科学系、图腾斯大学（TU Darmstadt）及应用网络安全国家研究中心ATHENE）；Department of Electrical Engineering, KU Leuven（电子工程系、鲁文大学）；Department of Computer Science, KU Leuven（计算机科学系、鲁文大学）
Faculty of Computer and Information Science, University of Ljubljana（卢布尔雅那大学计算机与信息科学学院）；Austrian Institute of Technology（奥地利技术研究所）；Faculty of Electrical Engineering, Czech Technical University in Prague（布拉格捷克理工大学电气工程学院）；University of Birmingham（伯明翰大学）；NICTA（澳大利亚国家信息与通信技术研究院）；Australian National University（澳大利亚国立大学）
This paper addresses the problem of single-target tracker performance evaluation. We consider the performance measures, the dataset and the evaluation system to be the most important components of tracker evaluation and propose requirements for each of them. The requirements are the basis of a new evaluation methodology that aims at a simple and easily interpretable tracker comparison. The ranking-based methodology addresses tracker equivalence in terms of statistical significance and practical differences. A fully-annotated dataset with per-frame annotations with several visual attributes is …
Stanford University（斯坦福大学）；Princeton University（普林斯顿大学）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometri…
MultiOrg: A Multi-rater Organoid-detection Dataset
Helmholtz AI（亥姆霍兹AI）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；Institute of Lung Health and Immunity (LHI)（肺部健康与免疫研究所）；Comprehensive Pneumology Center (CPC)（综合肺病学中心）；German Center for Lung Research (DZL)（德国肺部研究中心）；Technical University of Munich（慕尼黑工业大学）；Philipps-University（菲利普斯大学）
GAITEX: Human motion dataset of impaired gait and rehabilitation exercises using inertial and optical sensors
AI for Sensor Data Analytics Research Group（人工智能传感器数据解析研究组）；Ulm University of Applied Sciences（乌尔姆应用科学大学）；Biomechatronic Research Group（生物机械研究组）；Institute of Computer Science（计算机科学研究所）
Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening
Southern University of Science and Technology（南方科技大学）；The Hong Kong Polytechnic University（香港理工大学）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）
Do Large Language Models Understand Data Visualization Principles?
Universidad Torcuato Di Tella（托科托迪泰拉大学）；Consejo Nacional de Investigaciones Científicas y Técnicas（阿根廷国家科学与技术研究院）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）
National Institute of Advanced Industrial Science Technology (AIST)（国立高级工业科学技术研究所（AIST））；Tokyo Institute of Technology（东京工业大学）
Can we complete pre-training of Vision Transformers (ViT) without natural images and human-annotated labels? Although a pre-trained ViT seems to heavily rely on a large-scale dataset and human-annotated labels, recent large-scale datasets contain several problems in terms of privacy violations, inadequate fairness protection, and labor-intensive annotation. In the present paper, we pre-train ViT without any image collections and annotation labor. We experimentally verify that our proposed framework partially outperforms sophisticated Self-Supervised Learning (SSL) methods like SimCLRv2 and MoC…
Open-Vocabulary High-Resolution 3D (OVHR3D) Data Segmentation and Annotation Framework
USC Institute for Creative Technologies（南加州大学创意技术研究所）；Arizona State University（亚利桑那州立大学）；Colorado School of Mines（科罗拉多矿业大学）；University of Southern California（南加州大学）
Discriminative Image Generation with Diffusion Models for Zero-Shot Learning
Huazhong University of Science and Technology（华中科技大学）；Zhejiang University（浙江大学）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）
Philips Research（飞利浦研究院）；Skolkovo Institute of Science and Technology（斯科尔科沃科技学院）
Domain Adaptation (DA) methods are widely used in medical image segmentation tasks to tackle the problem of differently distributed train (source) and test (target) data. We consider the supervised DA task with a limited number of annotated samples from the target domain. It corresponds to one of the most relevant clinical setups: building a sufficiently accurate model on the minimum possible amount of annotated data. Existing methods mostly fine-tune specific layers of the pretrained Convolutional Neural Network (CNN). However, there is no consensus on which layers are better to fine-tune, e.…
Tel Aviv University（特拉维夫大学）；The Chaim Sheba Medical Center（夏姆谢巴医疗中心）；Bar-Ilan University（巴伊兰大学）
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs). Furthermore, we show that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classificatio…
Arizona State University（亚利桑那州立大学）；Barrow Neurological Institute（巴罗神经学研究所）；St Joseph’s Hospital and Medical Center（圣约瑟夫医院与医疗中心）；Irkutsk State Medical University（伊尔库茨克国立医科大学）
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence technology undergoing assessment for applications in brain tumor surgery. Despite its promising potential, interpreting the unfamiliar gray tone images of fluorescent stains can be difficult. Many of the CLE images can be distorted by motion, extremely low or high fluorescence signal, or obscured by red blood cell accumulation, and these can be interpreted as nondiagnostic. However, just one neat CLE image might suffice for intraoperative diagnosis of the tumor. While manual examination of thousands of nondiagnostic images…
Renal cell carcinoma (RCC) is a type of cancer that originates in the kidneys and is the most common type of kidney cancer in adults. It can be classified into several subtypes, including clear cell RCC, papillary RCC, and chromophobe RCC. In this study, an artificial intelligence model was developed and trained for classifying different subtypes of RCC using ResNet-18, a convolutional neural network that has been widely used for image classification tasks. The model was trained on a dataset of RCC histopathology images, which consisted of digital images of RCC surgical resection slides that w…
