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检索条件"任意字段=6th International Workshop on Graph-Based Representations in Pattern Recognition"

共 215 条记录，以下是1-10 订阅

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13th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2023

13th IAPR-TC-15 International Workshop on Graph-Based Repres...

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13th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2023

the proceedings contain 16 papers. the special focus in this conference is on . the topics include: Maximal Independent Sets for60;Pooling in60;graph Neural Networks;detecting Abnormal Communication patterns in&...

ISBN: (纸本)9783031427947

the proceedings contain 16 papers. the special focus in this conference is on . the topics include: Maximal Independent Sets for Pooling in graph Neural Networks;detecting Abnormal Communication patterns in IoT Networks Using graph Neural Networks;cell Segmentation of in situ Transcriptomics Data Using Signed graph Partitioning;graph-based Representation for Multi-image Super-Resolution;reducing the Computational Complexity of the Eccentricity Transform of a Tree;graph-based Deep Learning on the Swiss River Network;minimum Spanning Set Selection in graph Kernels;graph-based vs. Vector-based Classification: A Fair Comparison;a Practical Algorithm for Max-Norm Optimal Binary Labeling of graphs;an Efficient Entropy-based graph Kernel;GNN-DES: A New End-to-End Dynamic Ensemble Selection Method based on Multi-label graph Neural Network;C2N-ABDP: Cluster-to-Node Attention-based Differentiable Pooling;splitting Structural and Semantic Knowledge in graph Autoencoders for graph Regression;graph Normalizing Flows to Pre-image Free Machine Learning for Regression.

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ABDPool: Attention-based Differentiable Pooling 26

ABDPool: Attention-based Differentiable Pooling

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26th international Conference on pattern recognition / 8th international workshop on Image Mining - theory and Applications (IMTA)

作者： Liu, Yue Cui, Lixin Wang, Yue Bai, Lu Cent Univ Finance & Econ 39 South Coll Rd Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

graph Neural Networks (GNNs) have achieved state-of-the-art performance on a wide range of graph-based tasks such as graph classification and node classification. this is because the unique structure of GNNs allows them to effectively learn embeddings for unstructured data. One important operation for graph classification tasks is downsampling or pooling, which obtains graph representations from node representations. However, most GNNs are associated with global pooling, that can not learn hierarchical graph representations. Meanwhile, current hierarchical pooling methods have the shortcomings of unclear node assignment and uniform aggregation. To overcome these drawbacks, we propose an attention-based differentiable pooling operation in this paper, which can learn a hard cluster assignment for nodes and aggregate nodes in each cluster differently by introducing an attention mechanism. Experiments on standard graph classification benchmarks show that our proposed approach performs better when compared with other competing methods.

关键词： Aggregates Benchmark testing graph neural networks pattern recognition Task analysis Standards

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Leveraging Knowledge graph Embeddings to Enhance Contextual representations for Relation Extraction 17th

Leveraging Knowledge Graph Embeddings to Enhance Contextual ...

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17th international Conference on Document Analysis and recognition workshop (ICDAR)

作者： Laleye, Frejus A. A. Rakotoson, Loic Massip, Sylvain Opscidia Paris France

ISBN: (纸本)9783031415005;9783031415012

Relation extraction task is a crucial and challenging aspect of Natural Language Processing. Several methods have surfaced as of late, exhibiting notable performance in addressing the task;however, most of these approaches rely on vast amounts of data from large-scale knowledge graphs or language models pretrained on voluminous corpora. In this paper, we hone in on the effective utilization of solely the knowledge supplied by a corpus to create a high-performing model. Our objective is to showcase that by leveraging the hierarchical structure and relational distribution of entities within a corpus without introducing external knowledge, a relation extraction model can achieve significantly enhanced performance. We therefore proposed a relation extraction approach based on the incorporation of pretrained knowledge graph embeddings at the corpus scale into the sentence-level contextual representation. We conducted a series of experiments which revealed promising and very interesting results for our proposed approach. the obtained results demonstrated an outperformance of our method compared to context-based relation extraction models.

关键词： Relation extraction Knowledge graph Embeddings Contextual representation

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Data-driven Latent graph Structure Learning for Diagnosis of Alzheimer's Syndrome 26

Data-driven Latent Graph Structure Learning for Diagnosis of...

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26th international Conference on pattern recognition / 8th international workshop on Image Mining - theory and Applications (IMTA)

作者： Wang, Jianjia Wu, Chong Shanghai Univ Shanghai Inst Adv Commun & Data Sci Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Complex systems often have a latent graph structure. Studying the underlying graph structure will help us to analyze the mechanisms of complex phenomena. However, it is a challenging problem to learn effective graph structures from the data and apply them to downstream tasks. In this paper, we propose an end-to-end graph learning approach for Alzheimer's syndrome diagnosis based on functional magnetic resonance imaging (fMRI) data of brain regions, which is completely data-driven. the interactions between time-series of each brain region are represented as graph structures, and a multi-head attention mechanism is used to update the representations of the nodes. then, the graph structures are obtained from the feature sampling of the edges. Finally, the learned graph structure is combined with the left-out time-series data features and the node prior to completing the classification task of the brain network. In comparison with the latest research methods, our approach achieves higher classification accuracy.

关键词： Image edge detection Functional magnetic resonance imaging pattern recognition Task analysis Alzheimer's disease Complex systems

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Efficient Dense-graph Convolutional Network with Inductive Prior Augmentations for Unsupervised Micro-Gesture recognition 26

Efficient Dense-Graph Convolutional Network with Inductive P...

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26th international Conference on pattern recognition / 8th international workshop on Image Mining - theory and Applications (IMTA)

作者： Shah, Atif Chen, Haoyu Shi, Henglin Zhao, Guoying Univ Oulu Ctr Machine Vis & Signal Anal CMVS Oulu Finland

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Skeleton-based action/gesture recognition has already witnessed excellent progress on processing large-scale, laboratory-based datasets with pre-defined skeleton joint topology. However, it's still an unsolved task when it comes to real-world scenarios with practical limitations such as small-scaled dataset sizes, few-labeled samples, and various skeleton topologies. In this paper, we work on the recognition of micro-gestures, which are subtle body gestures collected in real-world scenarios. Specifically, we utilize contrastive learning to heritage the knowledge from known large-scale datasets for enhancing the learning on fewer samples of micro-gestures. To overcome the gap caused by various domain distributions and structure topologies between the datasets, we compute skeleton representations from augmented sequences via momentum-based efficient and scalable encoders as additional inductive priors. Importantly, we propose an effective dense-graph based unsupervised architecture that resorts to a queue-based dictionary to store positive and negative keys for better contrast with queries to learn substantially efficient and discriminant patterns in the feature space. Together with cross-dataset experimental results show that our model significantly improves the accuracies on two micro-gesture datasets, SMG by 7.4% and iMiGUE by 18.41% advocating its superiority.

关键词： Dictionaries Computer architecture Skeleton Robustness Data models Topology pattern recognition

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STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction recognition in Videos 26

STIT: Spatio-Temporal Interaction Transformers for Human-Obj...

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26th international Conference on pattern recognition / 8th international workshop on Image Mining - theory and Applications (IMTA)

作者： Almushyti, Muna Li, Frederick W. B. Univ Durham Dept Comp Sci Durham England Qassim Univ Educ Serv Buraydah Saudi Arabia

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recognizing human-object interactions is challenging due to their spatio-temporal changes. We propose the Spatio-Temporal Interaction Transformer-based (STIT) network to reason such changes. Specifically, spatial transformers learn humans and objects context at specific frame time. Temporal transformer then learns the relations at a higher level between spatial context representations at different time steps, capturing long-term dependencies across frames. We further investigate multiple hierarchy designs in learning human interactions. We achieved superior performance on Charades, Something-Something v1 and CAD-120 datasets, comparing to baseline models without learning human-object relations, or with prior graph-based networks. We also achieved state-of-the-art accuracy of 95.93% on CAD-120 dataset [1] by employing RGB data only.

关键词： Deep learning Transformers pattern recognition Videos Context modeling

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Quadratic Kernel Learning for Interpolation Kernel Machine based graph Classification 13th

Quadratic Kernel Learning for Interpolation Kernel Machine ...

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13th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2023

作者： Zhang, Jiaqi Liu, Cheng-Lin Jiang, Xiaoyi Faculty of Mathematics and Computer Science University of Münster Einsteinstrasse 62 Münster48149 Germany National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing10049 China

ISBN: (纸本)9783031427947

Interpolating classifiers interpolate all the training data and thus have zero training error. Recent research shows their fundamental importance for high-performance ensemble techniques. Interpolation kernel machines belong to the class of interpolating classifiers and do generalize well. they have been demonstrated to be a good alternative to support vector machine for graph classification. In this work we further improve their performance by considering multiple kernel learning. We establish a general scheme for achieving this goal. the current experimental work is done using quadratic kernel combination. Our experimental results demonstrate the performance boosting potential of our approach against the use of individual graph kernels. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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6th international workshop on PRedictive Intelligence In MEdicine, PRIME 2023

6th International Workshop on PRedictive Intelligence In MEd...

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6th international workshop on PRedictive Intelligence In MEdicine, PRIME 2023

ISBN: (纸本)9783031460043

the proceedings contain 24 papers. the special focus in this conference is on international workshop on PRedictive Intelligence In MEdicine. the topics include: Deep Survival Analysis in Multiple Sclerosis;Federated Multi-trajectory GNNs Under Data Limitations for Baby Brain Connectivity Forecasting;learning Task-Specific Morphological Representation for Pyramidal Cells via Mutual Information Minimization;dermoSegDiff: A Boundary-Aware Segmentation Diffusion Model for Skin Lesion Delineation;self-supervised Few-Shot Learning for Semantic Segmentation: An Annotation-Free Approach;imputing Brain Measurements Across Data Sets via graph Neural Networks;multi-input Vision Transformer with Similarity Matching;Federated Multi-domain GNN Network for Brain Multigraph Generation;an Ambient Intelligence-based Approach for Longitudinal Monitoring of Verbal and Vocal Depression Symptoms;Dynamic Depth-Supervised NeRF for Multi-view RGB-D Operating Room Videos;template-based Federated Multiview Domain Alignment for Predicting Heterogeneous Brain graph Evolution Trajectories from Baseline;Revisiting N-CNN for Clinical Practice;video-based Hand Pose Estimation for Remote Assessment of Bradykinesia in Parkinson’s Disease;more than Meets the Eye: Analyzing Anesthesiologists’ Visual Attention in the Operating Room Using Deep Learning Models;pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia;self-Supervised Learning of Gait-based Biomarkers;feature-based Transformer with Incomplete Multimodal Brain Images for Diagnosis of Neurodegenerative Diseases;repNet for Quantifying the Reproducibility of graph Neural Networks in Multiview Brain Connectivity Biomarker Discovery;synthA1c: Towards Clinically Interpretable Patient representations for Diabetes Risk Stratification;Confounding Factors Mitigation in Brain Age Prediction Using MRI with Deformation Fields;self-supervised Landmark Learning with Deformation Reconstruction and Cross-Subject Consistency Object

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A Novel Brain Connectivity-Powered graph Signal Processing Approach for Automated Detection of Schizophrenia from Electroencephalogram Signals 10th

A Novel Brain Connectivity-Powered Graph Signal Processing A...

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10th Biennial international Conference on pattern recognition and Machine Intelligence (PReMI)

作者： Pain, Subrata Vimal, Naincy Samanta, Debasis Sarma, Monalisa Indian Inst Technol Adv Technol Dev Ctr Kharagpur 721302 India Indian Inst Technol Dept Comp Sci Kharagpur 721302 India Indian Inst Technol Subir Chowdhury Sch Qual & Reliabil Kharagpur 721302 India

ISBN: (纸本)9783031451690;9783031451706

Schizophrenia is a severe neural disorder that affects around 24 million individuals globally. In this context, Electroencephalogram (EEG) signal-based analysis and automated screening for Schizophrenia (SZ) have gained importance. EEG-based Schizophrenia (SZ-EEG) analysis is traditionally done by extracting features from individual EEG electrodes' signals and utilizing these features for Machine Learning (ML)-based classification models. However, these methods do not exploit the Schizophrenia-induced alteration of functional brain connectivity between neuronal masses. the present study proposes a novel graph-signal (GS) representation of multi-channel SZ-EEG data that fully encompasses local brain activation and global interactions between brain regions. the proposed GS representation comprises the underlying connectivity network and the signal values on the network's vertices. Here, the EEG signal's entropy at each electrode is used as GS values, and a phase lag index (PLI)-based functional connectivity measure is utilized as the underlying connectivity network. Further, these connectivity-informed GSs are transformed to the spectral domain by the graph Fourier Transform (GFT), and relevant discriminatory features are extracted from them using the graph Signal Processing (GSP) technique. those features are fed to basic ML-based classification models. the efficacy of the proposed PLI-GSP framework is validated using a publicly available SZ-EEG dataset, and a 99.77% classification accuracy is achieved that outperforms most of the state-of-the-art models.

关键词： Schizophrenia Electroencephalogram Signals graphical representations graph Signal Processing Machine Learning

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Reducing the Computational Complexity of the Eccentricity Transform of a Tree 13th

Reducing the Computational Complexity of the Eccentricity...

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13th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2023

作者： Banaeyan, Majid Kropatsch, Walter G. Pattern Recognition and Image Processing Group TU Wien Vienna Austria

ISBN: (纸本)9783031427947

this paper proposes a novel approach to reduce the computational complexity of the eccentricity transform (ECC) for graph-based representation and analysis of shapes. the ECC assigns to each point within a shape its geodesic distance to the furthest point, providing essential information about the shape’s geometry, connectivity, and topology. Although the ECC has proven valuable in numerous applications, its computation using traditional methods involves heavy computational complexity. To overcome this limitation, we present a method that computes the ECC of a tree, significantly reducing the computational complexity from O(n2log(n) ) to O(b), where n and b are the numbers of vertices and branching points in the tree, respectively. Our method begins by computing the ECC for tree structures, which are simpler representations of shapes. Subsequently, we introduce the concept of a 3D curve that corresponds to a smooth shape without holes, enabling the computation of the ECC for more complex shapes. By leveraging the 3D curve representation, our method provides an upper-bound approximation of the ECC, which can be effectively utilized in various applications. the proposed approach not only preserves the valuable properties of the ECC but also significantly reduces the computational burden, making it a more efficient and practical solution for graph-based representation and analysis of shapes in both 2D and 3D contexts. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational complexity

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