Search Results for author: Edwin R. Hancock

Found 22 papers, 2 papers with code

Dual-modal Prior Semantic Guided Infrared and Visible Image Fusion for Intelligent Transportation System

no code implementations24 Mar 2024 Jing Li, Lu Bai, Bin Yang, Chang Li, Lingfei Ma, Lixin Cui, Edwin R. Hancock

Therefore, we propose a novel prior semantic guided image fusion method based on the dual-modality strategy, improving the performance of IVF in ITS.

Infrared And Visible Image Fusion Semantic Segmentation

SSHPool: The Separated Subgraph-based Hierarchical Pooling

no code implementations24 Mar 2024 Zhuo Xu, Lixin Cui, Yue Wang, Hangyuan Du, Lu Bai, Edwin R. Hancock

To this end, we commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs.

Graph Classification

Graph Representation Learning for Infrared and Visible Image Fusion

no code implementations1 Nov 2023 Jing Li, Lu Bai, Bin Yang, Chang Li, Lingfei Ma, Edwin R. Hancock

Then, GCNs are performed on the concatenate intra-modal NLss features of infrared and visible images, which can explore the cross-domain NLss of inter-modal to reconstruct the fused image.

Graph Representation Learning Infrared And Visible Image Fusion

AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks

no code implementations4 Mar 2023 Lixin Cui, Ming Li, Yue Wang, Lu Bai, Edwin R. Hancock

For pairwise graphs, the proposed AERK kernel is defined by computing a reproducing kernel based similarity between the quantum Shannon entropies of their each pair of aligned vertices.

Graph Classification

QESK: Quantum-based Entropic Subtree Kernels for Graph Classification

no code implementations10 Dec 2022 Lu Bai, Lixin Cui, Edwin R. Hancock

In this paper, we propose a novel graph kernel, namely the Quantum-based Entropic Subtree Kernel (QESK), for Graph Classification.

Graph Classification

HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification

no code implementations5 Nov 2022 Lu Bai, Lixin Cui, Yue Wang, Ming Li, Edwin R. Hancock

In this work, we propose a family of novel quantum kernels, namely the Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), for un-attributed graphs.

Graph Classification

Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective

1 code implementation CVPR 2022 Jiawei Zhang, Xiang Wang, Xiao Bai, Chen Wang, Lei Huang, Yimin Chen, Lin Gu, Jun Zhou, Tatsuya Harada, Edwin R. Hancock

The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points.

Contrastive Learning Stereo Matching

HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects

no code implementations19 Nov 2020 Suihanjin Yu, Youmin Zhang, Chen Wang, Xiao Bai, Liang Zhang, Edwin R. Hancock

To address this problem, we introduce a lightweight but effective Global Matching Component (GMC) to grab global matching features.

Optical Flow Estimation

Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks

no code implementations13 Oct 2020 Yue Wang, Zhuo Xu, Lu Bai, Yao Wan, Lixin Cui, Qian Zhao, Edwin R. Hancock, Philip S. Yu

To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods.

Event Extraction TAG

A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs

no code implementations8 Feb 2020 Lu Bai, Lixin Cui, Edwin R. Hancock

First, it incorporates the locational correspondence information between graphs into the kernel computation, and thus overcomes the shortcoming of ignoring structural correspondences arising in most R-convolution kernels.

Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis

no code implementations21 Oct 2019 Lu Bai, Lixin Cui, Lixiang Xu, Yue Wang, Zhihong Zhang, Edwin R. Hancock

With the dominant entropy time series for each pair of financial networks to hand, we develop a similarity measure based on the classical dynamic time warping framework, for analyzing the financial time-varying networks.

Dynamic Time Warping Time Series +1

Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification

no code implementations6 Apr 2019 Lu Bail, Lixin Cui, Yuhang Jiao, Luca Rossi, Edwin R. Hancock

In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification.

General Classification Graph Classification

Fused Lasso for Feature Selection using Structural Information

no code implementations26 Feb 2019 Lu Bai, Lixin Cui, Yue Wang, Philip S. Yu, Edwin R. Hancock

To overcome these issues, we propose a new feature selection method using structural correlation between pairwise samples.

feature selection Time Series Analysis

Learning Vertex Convolutional Networks for Graph Classification

no code implementations26 Feb 2019 Lu Bai, Lixin Cui, Shu Wu, Yuhang Jiao, Edwin R. Hancock

In this paper, we develop a new aligned vertex convolutional network model to learn multi-scale local-level vertex features for graph classification.

General Classification Graph Classification

Identifying The Most Informative Features Using A Structurally Interacting Elastic Net

no code implementations8 Sep 2018 Lixin Cui, Lu Bai, Zhihong Zhang, Yue Wang, Edwin R. Hancock

With the feature graphs to hand, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature graph representation.

feature selection

Graph Convolutional Neural Networks based on Quantum Vertex Saliency

no code implementations4 Sep 2018 Lu Bai, Yuhang Jiao, Luca Rossi, Lixin Cui, Jian Cheng, Edwin R. Hancock

This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes.

General Classification Graph Classification

Example-Based Modeling of Facial Texture From Deficient Data

no code implementations ICCV 2015 Arnaud Dessein, William A. P. Smith, Richard C. Wilson, Edwin R. Hancock

We present an approach to modeling ear-to-ear, high-quality texture from one or more partial views of a face with possibly poor resolution and noise.

Super-Resolution

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