Search
Search Results
-
Multi-type clustering using regularized tensor decomposition
Geospatial analytics increasingly rely on data fusion methods to extract patterns from data; however robust results are difficult to achieve because...
-
Incorporating self-attentions into robust spatial-temporal graph representation learning against dynamic graph perturbations
This paper proposes a Robust Spatial-Temporal Graph Neural Network (RSTGNN), which overcomes the limitations faced by graph-based models against...
-
Cross-modality person re-identication with triple-attentive feature aggregation
Cross-modal person re-identification between the visible (RGB) modality and infrared (IR) modality is extremely important for nighttime surveillance...
-
Multi-view graph representation learning for hyperspectral image classification with spectral–spatial graph neural networks
Hyperspectral image (HSI) classification benefits from effectively handling both spectral and spatial features. However, deep learning (DL) models,...
-
Learning deep convolutional descriptor aggregation for efficient visual tracking
Visual trackers have achieved a high-level performance from deep features, but many limitations remain. Online trackers suffer from low speed while...
-
Automatic EEG channel selection for multiclass brain-computer interface classification using multiobjective improved firefly algorithm
Multichannel Electroencephalography-based Brain-Computer Interface (BCI) systems facilitate a communicating medium between the human brain and the...
-
Measuring Decision Confidence Levels from EEG Using a Spectral-Spatial-Temporal Adaptive Graph Convolutional Neural Network
Decision confidence can reflect the correctness of people’s decisions to some extent. To measure the reliability of human decisions in an objective...
-
BS-GAENets: Brain-Spatial Feature Learning Via a Graph Deep Autoencoder for Multi-modal Neuroimaging Analysis
The obsession with how the brain and behavior are related is a challenge for cognitive neuroscience research, for which functional magnetic resonance...
-
Common Business Big Data Management and Decision Model
In this chapter, we study the application of self-expression based subset selection method in cluster multi-task learning. Multitasking learning aims...
-
Multi-facial patches aggregation network for facial expression recognition and facial regions contributions to emotion display
In this paper, an approach for Facial Expressions Recognition (FER) based on a multi-facial patches (MFP) aggregation network is proposed. Deep...
-
Learning multi-tasks with inconsistent labels by using auxiliary big task
Multi-task learning is to improve the performance of the model by transferring and exploiting common knowledge among tasks. Existing MTL works mainly...
-
Turning traffic volume imputation for persistent missing patterns with GNNs
Traffic volume data at fixed detectors are of great importance to track the time-varying states of urban traffic, and the volume of each turning...
-
TA-GAN: transformer-driven addiction-perception generative adversarial network
The identification of addiction-related brain connections using functional magnetic resonance imaging (fMRI) is essential for comprehending the...
-
MGFNet: A Multi-granularity Feature Fusion and Mining Network for Visible-Infrared Person Re-identification
Visible-infrared person re-identification (VI-ReID) aims to match the same pedestrian in different forms captured by the visible and infrared...
-
Reconstructing 36 Years of Spatiotemporal Dynamics of Slums in Brazil by Integrating EO and Census Data
Officially, by 2019, more than five million households in 734 Brazilian municipalities were in slums, accounting for 7.8% of the total households....
-
Geometry and Topology
The representation of the geometrical properties of spatial objects and their structural aspects (topology) is crucial for GIS operations, analyses,...
-
A cross-layered cluster embedding learning network with regularization for multivariate time series anomaly detection
The devices deployed across diverse industrial scenarios have generated significant network traffic related to time. The system’s irregular operation...
-
Apply Multivariate Time Series Approaches for Forecasting Vietnam Index 30
The stock market is an attractive channel for many investment funds. The stock indices of several largest capitalized companies are the key...
-
Enabling Robust SLAM for Mobile Robots with Sensor Fusion
Simultaneous Localization and Map** (SLAM) is a fundamental problem in robotics. Over the past three decades, researchers have made significant...
-
Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects
Moving object segmentation (MOS), aiming at segmenting moving objects from video frames, is an important and challenging task in computer vision and...