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Found 10 papers, 0 papers with code
Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking
The core of Siamese feature matching is how to assign high feature similarity on the corresponding points between the template and search area for precise object localization.
Hyperspectral Image Classification With Contrastive Graph Convolutional Network
To enhance the feature representation ability, in this paper, a GCN model with contrastive learning is proposed to explore the supervision signals contained in both spectral information and spatial relations, which is termed Contrastive Graph Convolutional Network (ConGCN), for HSI classification.
Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning
In this paper, we propose Cocktail Edge Caching, that tackles the dynamic popularity and heterogeneity through ensemble learning.
Time Series Counterfactual Inference with Hidden Confounders
We present augmented counterfactual ordinary differential equations (ACODEs), a new approach to counterfactual inference on time series data with a focus on healthcare applications.
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network
In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance.
D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models.
Big Data Meet Cyber-Physical Systems: A Panoramic Survey
{Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics.
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Multi-Rate Multivariate Time Series (MR-MTS) are the multivariate time series observations which come with various sampling rates and encode multiple temporal dependencies.
Tensor Regression Meets Gaussian Processes
Low-rank tensor regression, a new model class that learns high-order correlation from data, has recently received considerable attention.
Low-Rank Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image Denoising
Patch-based low-rank models have shown effective in exploiting spatial redundancy of natural images especially for the application of image denoising.
