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Found 33 papers, 13 papers with code
Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection
These factors also limit the performance of the well-known low-rank representation (LRR) models in terms of robustness on the separation of background and target features and the reliance on manual parameter selection.
SpectralGPT: Spectral Remote Sensing Foundation Model
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e. g., single cities or regions).
Demystifying the Performance of Data Transfers in High-Performance Research Networks
High-speed research networks are built to meet the ever-increasing needs of data-intensive distributed workflows.
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface at a distance of data acquisition devices, such as aircraft, spacecraft, and satellite.
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way.
Experimental quantum adversarial learning with programmable superconducting qubits
Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.
SimPO: Simultaneous Prediction and Optimization
Concretely, predictive models are often employed in estimating the parameters for the input values that are utilized for optimization models as isolated processes.
SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies.
FCCDN: Feature Constraint Network for VHR Image Change Detection
Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning.
Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing
Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing, yet their ability to simultaneously generalize various spectral variabilities and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various spectral variabilities.
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation
Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor using the intermediate data distributions generated for the two domains, which includes an image-to-image translator and a shared-weighted segmentation network.
An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery
Semantic segmentation is an essential part of deep learning.
Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing
Tensor-based methods have been widely studied to attack inverse problems in hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented as a third-order tensor, which can perfectly retain the spatial information in the image.
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations
Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels.
Dissecting Energy Budget of a Gamma-Ray Burst Fireball
The jet composition and radiative efficiency of GRBs are poorly constrained from the data.
High Energy Astrophysical Phenomena
SLCRF: Subspace Learning with Conditional Random Field for Hyperspectral Image Classification
In SLCRF, first, the 3D convolutional autoencoder (3DCAE) is introduced to remove the redundant information in HSI pixels.
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR Data
To overcome this limitation, we present a simple but effective multimodal DL baseline by following a deep encoder–decoder network architecture, EndNet for short, for the classification of hyperspectral and light detection and ranging (LiDAR) data.
More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
In particular, we also investigate a special case of multi-modality learning (MML) -- cross-modality learning (CML) that exists widely in RS image classification applications.
Graph Convolutional Networks for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification, owing to their ability to capture spatial-spectral feature representations.
Spectral Superresolution of Multispectral Imagery with Joint Sparse and Low-Rank Learning
However, the ability in the fusion of HS and MS images remains to be improved, particularly in large-scale scenes, due to the limited acquisition of HS images.
Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution
In this work, an unsupervised deep learning-based fusion method - HyCoNet - that can solve the problems in HSI-MSI fusion without the prior PSF and SRF information is proposed.
Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning
The model provides automated recognition of given scans and generation of reports.
HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation
In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate.
MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling
Therefore, the proposed network has a dual-branch architecture that tackles two tasks: 1) a segmentation sub-network aiming to generate the prostate segmentation, and 2) a voxel-metric learning sub-network aiming to improve the quality of the learned feature space supervised by a metric loss.
On the FRB luminosity function -- II. Event rate density
Assuming a Schechter luminosity function form, we infer (at the 95% confidence level) that the characteristic FRB event rate density at the upper cut-off luminosity $L^*=2. 9_{-1. 7}^{+11. 9}\times10^{44}\,\rm erg\, s^{-1}$ is $\phi^*=339_{-313}^{+1074}\,\rm Gpc^{-3}\, yr^{-1}$, the power-law index is $\alpha=-1. 79_{-0. 35}^{+0. 31}$, and the lower cut-off luminosity is $L_0\le9. 1\times10^{41}\,\rm erg\, s^{-1}$.
High Energy Astrophysical Phenomena Cosmology and Nongalactic Astrophysics
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning
The land-use map is an important data that can reflect the use and transformation of human land, and can provide valuable reference for land-use planning.
Second Repeating FRB 180814.J0422+73: Ten-year Fermi-LAT Upper limits and Implications
The second repeating fast radio burst source, FRB 180814. J0422+73, was detected recently by the CHIME collaboration.
High Energy Astrophysical Phenomena
Investigation of the asteroid-neutron star collision model for the repeating fast radio bursts
The origin of fast radio bursts (FRBs) is still a mystery.
High Energy Astrophysical Phenomena Earth and Planetary Astrophysics
A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions
In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in one short question are difficult to be discovered entirely and the incomplete information in short text makes entity linking hard to implement.
On the normalised FRB luminosity function
In this paper, we measure the normalised luminosity function of FRBs.
High Energy Astrophysical Phenomena
Learning to score the figure skating sports videos
This paper targets at learning to score the figure skating sports videos.
