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GraphDescriptor: Augmenting Node-Link Diagrams With Textual Descriptions

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GraphDescriptor: Augmenting Node-Link Diagrams With Textual ...

Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Jiacheng Pan Zihan Yan Zihan Zhou Xiaodong Zhao Shenghui Cheng Dongming Han Jian Chen Mingliang Xu Wei Chen State Key Lab of CAD&CG Zhejiang University MIT Media Lab Cambridge MA USA Research Center for Industries of the Future and School of Engineering Westlake University Faculty of Computer Science and Engineering The Ohio State University USA Henan Institute of Advanced Technology Zhengzhou University China Laboratory of Art and Archaeology Image (Zhejiang University) Ministry of Education China

Node-link diagrams are the most popular form for graph visualization. Yet, salient information of a node-link diagram cannot be fully depicted by solely presenting the visualization. We propose to augment node-link diagrams by creating textual descriptions for interested information. We conduct an expert review and a user interview to identify six requirements of generated interpretations, including three requirements for connection extraction and three requirements for visual expression. Our solution, GraphDescriptor, generates textual descriptions with two stages: feature extraction and description generation. The first one identifies and extracts features of node-link diagrams, like node connections, visual designs, and types of graph layouts. The second stage creates a group of hierarchical sentences based on a pre-defined schema. To the best of our knowledge, our approach is the first attempt to generate textual descriptions automatically. Three use cases and the in-lab user study confirm the superiority of our approach.

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Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack 31

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Robust Ensemble Model Training via Random Layer Sampling Aga...

31st British Machine Vision Conference, BMVC 2020

作者： Lee, Hakmin Lee, Hong Joo Kim, Seong Tae Ro, Yong Man Image and Video System Lab Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Computer Aided Medical Procedures Technical University of Munich Munich Germany

Deep neural networks have achieved substantial achievements in several computer vision areas, but have vulnerabilities that are often fooled by adversarial examples that are not recognized by humans. This is an important issue for security or medical applications. In this paper, we propose an ensemble model training framework with random layer sampling to improve the robustness of deep neural networks. In the proposed training framework, we generate various sampled model through the random layer sampling and update the weight of the sampled model. After the ensemble models are trained, it can hide the gradient efficiently and avoid the gradient-based attack by the random layer sampling method. To evaluate our proposed method, comprehensive and comparative experiments have been conducted on three datasets. Experimental results show that the proposed method improves the adversarial robustness. © 2020. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Deep neural networks

GraphFederator: Federated Visual Analysis for Multi-party Graphs

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GraphFederator: Federated Visual Analysis for Multi-party Gr...

Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Dongming Han Haiyang Zhu Wei Chen Rusheng Pan Yijing Liu Jiehui Zhou Haozhe Feng Tianye Zhang Xumeng Wang Minfeng Zhu Jianrong Tao Changjie Fan Xiaolong Luke Zhang State Key Lab of CAD&CG Zhejiang University China Hithink RoyalFlush Information Network Co. Ltd. China Wuchan Zhongda Digital Technology Co. Ltd. China Ministry of Education Laboratory of Art and Archaeology Image (Zhejiang University) China College of Computer Science Nankai University China Zhejiang University China Fuxi AI Lab Netease China Pennsylvania State University USA

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

This paper presents GraphFederator, a novel approach to construct federated representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for federated modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a Federated Graph Representation Model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization tools for federated visualization, exploration, and analysis of multi-party graphs. Experimental results on two datasets demonstrate the effectiveness of our approach.

关键词： Visualization Analytical models Federated learning Data visualization Data models Encryption

An efficient solution to the relative pose estimation with a common direction

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An efficient solution to the relative pose estimation with a...

2020 IEEE International Conference on Robotics and Automation, ICRA 2020

作者： Ding, Yaqing Yang, Jian Kong, Hui Nanjing University of Science and Technology Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security China

ISBN: (纸本)9781728173955

In this paper, we propose an efficient solution to the calibrated camera motion estimation with a common direction. This case is relevant to smart phones, tablets, and other camera-IMU (Inertial measurement unit) systems, which have accelerometers to measure the gravity direction. We can align one of the axes of the camera with this common direction so that the relative rotation between the views reduces to only 1DOF (degree of freedom). This allows us to use only three point correspondences for relative pose estimation. Unlike previous work, we derive new constraints on the simplified essential matrix using an elimination strategy based on Gröbner basis. In this case, computing the coefficients of these constraints require less computation and we only need to solve a polynomial eigenvalue problem. We show detailed analyses and comparisons against the existing 3-point algorithms, with satisfactory results obtained. © 2020 IEEE.

关键词： Cameras

A general elimination strategy for camera motion estimation

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A general elimination strategy for camera motion estimation

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yaqing Ding Yingna Su Chengzhong Xu Jian Yang Hui Kong Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Faculty of Science and Technology University of Macau Macau SAR China

Camera motion estimation, such as relative pose estimation and absolute pose estimation, are fundamental problems in computer vision and robotics. To obtain the motion parameters, classical methods rely on studying the properties of the geometric matrices, e.g., rotation matrix, essential matrix, homography matrix. The well known five-point algorithm was successfully derived using the singular constraint and trace constraints on the essential matrix. However, finding all the algebraic constraints is not always trivial for some recent problems. In this paper, we propose a simple and general technique to find complete algebraic constraints so that we can derive efficient algorithms. We show that using the quaternion to formulate the rotation matrix we can eliminate any unknowns from the original equations and obtain constraints on the rest of the unknowns based on Gröbner basis. We demonstrate that this approach can be applied to almost all the camera motion estimation and show its improvement compared to the existing methods. Further more, based on this elimination technique, we exploit new constraints for the relative pose estimation with gravity prior, and derive a new globally optimal algorithm to this problem. We compare our algorithm with the state-of-the-art methods on both synthetic and real-world data, and show the benefits including accuracy and efficiency.

关键词： Computer vision Automation Motion estimation Quaternions Conferences Pose estimation Robot vision systems

UnDeepLIO: Unsupervised deep lidar-inertial odometry

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arXiv 2021年

作者： Tu, Yiming Xie, Jin PCA Lab. Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Extensive research efforts have been dedicated to deep learning based odometry. Nonetheless, few efforts are made on the unsupervised deep lidar odometry. In this paper, we design a novel framework for unsupervised lidar odometry with the IMU, which is never used in other deep methods. First, a pair of siamese LSTMs are used to obtain the initial pose from the linear acceleration and angular velocity of IMU. With the initial pose, we perform the rigid transform on the current frame and align it to the last frame. Then we extract vertex and normal features from the transformed point clouds and its normals. Next a two-branch attention module is proposed to estimate residual rotation and translation from the extracted vertex and normal features, respectively. Finally, our model outputs the sum of initial and residual poses as the final pose. For unsupervised training, we introduce an unsupervised loss function which is employed on the voxelized point clouds. The proposed approach is evaluated on the KITTI odometry estimation benchmark and achieves comparable performances against other state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

Planning with learned dynamic model for unsupervised point cloud registration

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arXiv 2021年

作者： Jiang, Haobo Qian, Jianjun Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

Point cloud registration is a fundamental problem in 3D computer vision. In this paper, we cast point cloud registration into a planning problem in reinforcement learning, which can seek the transformation between the source and target point clouds through trial and error. By modeling the point cloud registration process as a Markov decision process (MDP), we develop a latent dynamic model of point clouds, consisting of a transformation network and evaluation network. The transformation network aims to predict the new transformed feature of the point cloud after performing a rigid transformation (i.e., action) on it while the evaluation network aims to predict the alignment precision between the transformed source point cloud and target point cloud as the reward signal. Once the dynamic model of the point cloud is trained, we employ the cross-entropy method (CEM) to iteratively update the planning policy by maximizing the rewards in the point cloud registration process. Thus, the optimal policy, i.e., the transformation between the source and target point clouds, can be obtained via gradually narrowing the search space of the transformation. Experimental results on ModelNet40 and 7Scene benchmark datasets demonstrate that our method can yield good registration performance in an unsupervised manner. Copyright © 2021, The Authors. All rights reserved.

关键词： Dynamic models

3D siamese voxel-to-BEV tracker for sparse point clouds

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arXiv 2021年

作者： Hui, Le Wang, Lingpeng Cheng, Mingmei Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

3D object tracking in point clouds is still a challenging problem due to the sparsity of LiDAR points in dynamic environments. In this work, we propose a Siamese voxel-to-BEV tracker, which can significantly improve the tracking performance in sparse 3D point clouds. Specifically, it consists of a Siamese shape-aware feature learning network and a voxel-to-BEV target localization network. The Siamese shape-aware feature learning network can capture 3D shape information of the object to learn the discriminative features of the object so that the potential target from the background in sparse point clouds can be identified. To this end, we first perform template feature embedding to embed the template’s feature into the potential target and then generate a dense 3D shape to characterize the shape information of the potential target. For localizing the tracked target, the voxel-to-BEV target localization network regresses the target’s 2D center and the z-axis center from the dense bird’s eye view (BEV) feature map in an anchor-free manner. Concretely, we compress the voxelized point cloud along z-axis through max pooling to obtain a dense BEV feature map, where the regression of the 2D center and the z-axis center can be performed more effectively. Extensive evaluation on the KITTI and nuScenes datasets shows that our method significantly outperforms the current state-of-the-art methods by a large margin. Code is available at https://***/fpthink/V2B. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

AIM 2024 Sparse Neural Rendering Challenge: Methods and Results

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arXiv 2024年

作者： Nazarczuk, Michal Catley-Chandar, Sibi Tanay, Thomas Shaw, Richard Pérez-Pellitero, Eduardo Timofte, Radu Yan, Xing Wang, Pan Guo, Yali Wu, Yongxin Cai, Youcheng Yang, Yanan Li, Junting Zhou, Yanghong Mok, P.Y. He, Zongqi Xiao, Zhe Chan, Kin-Chung Goshu, Hana Lebeta Yang, Cuixin Dong, Rongkang Xiao, Jun Lam, Kin-Man Hao, Jiayao Gao, Qiong Zu, Yanyan Zhang, Junpei Jiao, Licheng Liu, Xu Purohit, Kuldeep Huawei Noah's Ark Laboratory Hong Kong University of Würzburg Germany Hefei University of Technology China University of Science and Technology of China China The Hong Kong Polytechnic University Hong Kong Research Centre of Textiles for Future Fashion Hong Kong Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong Intelligent Perception and Image Understanding Lab Xidian University China Google Mountain View United States

This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript focuses on the competition set-up, the proposed methods and their respective results. The challenge aims at producing novel camera view synthesis of diverse scenes from sparse image observations. It is composed of two tracks, with differing levels of sparsity;3 views in Track 1 (very sparse) and 9 views in Track 2 (sparse). Participants are asked to optimise objective fidelity to the ground-truth images as measured via the Peak Signal-to-Noise Ratio (PSNR) metric. For both tracks, we use the newly introduced Sparse Rendering (SpaRe) dataset [22] and the popular DTU MVS dataset [1]. In this challenge, 5 teams submitted final results to Track 1 and 4 teams submitted final results to Track 2. The submitted models are varied and push the boundaries of the current state-of-the-art in sparse neural rendering. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2024, The Authors. All rights reserved.

关键词： Rendering (computer graphics)