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Found 70 papers, 27 papers with code
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing Images
Common solutions include cloud removal for optical data or directly using Synthetic aperture radar (SAR) data in the target domain.
STDCformer: A Transformer-Based Model with a Spatial-Temporal Causal De-Confounding Strategy for Crowd Flow Prediction
We further decompose this cross-time transformation into three processes: (1) Encoding ($E$): learning the intrinsic representation of observations, (2) Cross-Time Mapping ($M$): transforming past representations into future representations, and (3) Decoding ($D$): reconstructing future observations from the future representations.
Towards Single-Lens Controllable Depth-of-Field Imaging via All-in-Focus Aberration Correction and Monocular Depth Estimation
A Depth-aware Controllable DoF Imaging (DCDI) framework is proposed equipped with All-in-Focus (AiF) aberration correction and monocular depth estimation, where the recovered image and corresponding depth map are utilized to produce imaging results under diverse DoFs of any high-end lens via patch-wise convolution.
PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images
There's a strong need for an adaptable approach that can effectively handle different 3D medical structures and imaging modalities.
IFShip: A Large Vision-Language Model for Interpretable Fine-grained Ship Classification via Domain Knowledge-Enhanced Instruction Tuning
To address this issue, we propose a large vision-language model (LVLM) named IFShip for interpretable fine-grained ship classification.
SeFi-CD: A Semantic First Change Detection Paradigm That Can Detect Any Change You Want
The existing change detection(CD) methods can be summarized as the visual-first change detection (ViFi-CD) paradigm, which first extracts change features from visual differences and then assigns them specific semantic information.
RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image Understanding
Under the new LaGD paradigm, the old datasets, which have led to advances in RSI intelligence understanding in the last decade, are no longer suitable for fire-new tasks.
STAR: A First-Ever Dataset and A Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery
This paper constructs a large-scale dataset for SGG in large-size VHR SAI with image sizes ranging from 512 x 768 to 27, 860 x 31, 096 pixels, named STAR (Scene graph generaTion in lArge-size satellite imageRy), encompassing over 210K objects and over 400K triplets.
Homogeneous Tokenizer Matters: Homogeneous Visual Tokenizer for Remote Sensing Image Understanding
Compared to Patch Embed, which requires more than one hundred tokens for one image, HOOK requires only 6 and 8 tokens for sparse and dense tasks, respectively, resulting in efficiency improvements of 1. 5 to 2. 8 times.
LSTTN: A Long-Short Term Transformer-based Spatio-temporal Neural Network for Traffic Flow Forecasting
Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of current traffic flow prediction models.
SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameters
Deep neural network-based Synthetic Aperture Radar (SAR) target recognition models are susceptible to adversarial examples.
HSONet:A Siamese foreground association-driven hard case sample optimization network for high-resolution remote sensing image change detection
To deal with this imbalance, we propose an equilibrium optimization loss function to regulate the optimization focus of the foreground and background, determine the hard case samples through the distribution of the loss values, and introduce dynamic weights in the loss term to gradually shift the optimization focus of the loss from the foreground to the background hard cases as the training progresses.
AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Ten Modalities via Language as a Reference Framework
To maintain modality autonomy, AllSpark uses modality-specific encoders to extract the tokens of various spatio-temporal modalities.
A Multi-level Distillation based Dense Passage Retrieval Model
In this paper, we propose a dense retrieval model called MD2PR based on multi-level distillation.
Image Quality, Uniformity and Computation Improvement of Compressive Light Field Displays with U-Net
We apply the U-Net model for compressive light field synthesis.
CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph
To estimate the DE, since the DE are generated through two paths (grab the attention assigned to neighbors and reduce the self-attention of the central node), we use Total Effect to model DE, which is a kind of causal estimand and can be estimated from intervened data; To weaken the DE, we identify the neighbors with the highest DE (we call them Distraction Neighbors) and remove them.
Ranked #2 on
Node Classification
on Cornell
MSFormer: A Skeleton-multiview Fusion Method For Tooth Instance Segmentation
Furthermore, the segmentation accuracy is improved by 2. 4%-5. 5% with the increasing volume of training data.
Using Global Land Cover Product as Prompt for Cropland Mapping via Visual Foundation Model
This simplifies the domain adaption from generic to specific scenes during model reasoning processes.
AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning
Continual lifelong learning is an machine learning framework inspired by human learning, where learners are trained to continuously acquire new knowledge in a sequential manner.
GraSS: Contrastive Learning with Gradient Guided Sampling Strategy for Remote Sensing Image Semantic Segmentation
Based on this, we propose contrastive learning with Gradient guided Sampling Strategy (GraSS) for RSI semantic segmentation.
BCE-Net: Reliable Building Footprints Change Extraction based on Historical Map and Up-to-Date Images using Contrastive Learning
This contrastive learning strategy allowed us to inject the semantics of buildings into a pipeline for the detection of changes, which is achieved by increasing the distinguishability of features of buildings from those of non-buildings.
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples
Then we compute the structural equivalence of node pairs based on their topological features.
False: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image
This indicates that the SSCL model has the ability to self-differentiate FNS and that the FALSE effectively mitigates the SCI in self-supervised contrastive learning.
Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works
Deep learning has achieved great success in learning features from massive remote sensing images (RSIs).
STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph
We further propose spatial-temporal Granger causality (STGC) to express TCR, which models global and dynamic spatial dependence.
HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation
Hence, this work proposes a hard-negative sample aware self-supervised contrastive learning method to pre-train the model for semantic segmentation.
Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations
iGCL designs the invariant-discriminative loss (ID loss) to learn invariant and discriminative representations.
TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning
We believe that a general model which is trained by a label-free and task-independent way may be the next paradigm for RSIU and hope the insights distilled from this study can help to foster the development of an original vision model for RSIU.
Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds
With that, we develop a large-scale synthetic scene flow dataset GTA-SF.
Image Segmentation with Adaptive Spatial Priors from Joint Registration
In medical imaging for instance, intensity inhomogeneity and noise are common.
Overcome Anterograde Forgetting with Cycled Memory Networks
This work proposes a general framework named Cycled Memory Networks (CMN) to address the anterograde forgetting in neural networks for lifelong learning.
Reviewing continual learning from the perspective of human-level intelligence
Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism.
Learning by Active Forgetting for Neural Networks
This paper presents a learning model by active forgetting mechanism with artificial neural networks.
Curvature Graph Neural Network
And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature.
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images
Supervised learning for semantic segmentation requires a large number of labeled samples, which is difficult to obtain in the field of remote sensing.
Generating Multi-scale Maps from Remote Sensing Images via Series Generative Adversarial Networks
The series strategy avoids RS-m inconsistency as inputs are high-resolution large-scale RSIs, and reduces the distribution gap in multi-scale map generation through similar pixel distributions among multi-scale maps.
Graph Information Vanishing Phenomenon inImplicit Graph Neural Networks
A class of GNNs solves this problem by learning implicit weights to represent the importance of neighbor nodes, which we call implicit GNNs such as Graph Attention Network.
On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control
Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid.
Optimization and Control Systems and Control Systems and Control
KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks.
Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images
Finally, multi-view information from both the nadir and oblique images is used in a robust voting procedure to label changes in existing buildings.
AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting
Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation.
Hierarchical Paired Channel Fusion Network for Street Scene Change Detection
Based on the MPFL strategy, our framework achieves a novel approach to adapt to the scale and location diversities of the scene change regions.
Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples
With the development of deep learning, supervised learning methods perform well in remote sensing images (RSIs) scene classification.
RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification
On the one hand, RS-MetaNet raises the level of learning from the sample to the task by organizing training in a meta way, and it learns to learn a metric space that can well classify remote sensing scenes from a series of tasks.
Urban Traffic Flow Forecast Based on FastGCRNN
Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks.
Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources
It also demonstrates that this method can help to efficiently obtain replanning schemes based on original scheme in dynamic environments.
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting
In this study, an attention temporal graph convolutional network (A3T-GCN) traffic forecasting method was proposed to simultaneously capture global temporal dynamics and spatial correlations.
DAPnet: A Double Self-attention Convolutional Network for Point Cloud Semantic Labeling
The incorporation of the double self-attention module has an average of 7\% improvement on the pre-class accuracy.
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information.
Deep Fusion of Local and Non-Local Features for Precision Landslide Recognition
The second uses a scale attention mechanism to guide the up-sampling of features from the coarse level by a learned weight map.
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification
The feature-learning procedure of CNN largely depends on the architecture of CNN.
SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images
High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location.
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation
It is due to two facts: first, as the model learns more tasks, the intersection of the low-error parameter subspace satisfying for these tasks becomes smaller or even does not exist; second, when the model learns a new task, the cumulative error keeps increasing as the model tries to protect the parameter configuration of previous tasks from interference.
Adversarial Example in Remote Sensing Image Recognition
This added adversarial perturbation image is called an adversarial example, which poses a serious security problem for systems based on CNN model recognition results.
Spatial Information Inference Net: Road Extraction Using Road-Specific Contextual Information
The validation experiments using three large datasets of very high-resolution (VHR) satellite imagery show that the proposed method can improve road extraction accuracy and provide an output that is more in line with human expectations.
MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery
This paper proposes a novel multi attending path neural network (MAP-Net) for accurately extracting multiscale building footprints and precise boundaries.
Volume Preserving Image Segmentation with Entropic Regularization Optimal Transport and Its Applications in Deep Learning
Image segmentation with a volume constraint is an important prior for many real applications.
Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis
Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems.
Probabilistic Load Forecasting via Point Forecast Feature Integration
In the first stage, all related features are utilized to train a point forecast model and also obtain the feature importance.
Program Classification Using Gated Graph Attention Neural Network for Online Programming Service
The online programing services, such as Github, TopCoder, and EduCoder, have promoted a lot of social interactions among the service users.
Understanding the Importance of Single Directions via Representative Substitution
Understanding the internal representations of deep neural networks (DNNs) is crucal to explain their behavior.
A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome
In this regard, we propose the Adversarial Feature Genome (AFG), a novel type of data that contains both the differences and features about classes.
Overcoming Catastrophic Forgetting by Soft Parameter Pruning
In this paper, we proposed a Soft Parameters Pruning (SPP) strategy to reach the trade-off between short-term and long-term profit of a learning model by freeing those parameters less contributing to remember former task domain knowledge to learn future tasks, and preserving memories about previous tasks via those parameters effectively encoding knowledge about tasks at the same time.
Understanding the Importance of Single Directions via Representative Substitution
Understanding the internal representations of deep neural networks (DNNs) is crucal to explain their behavior.
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence.
Ranked #4 on
Traffic Prediction
on SZ-Taxi
Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled.
On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition
Owing to its superiority in feature representation, DCNN has exhibited remarkable performance in scene recognition of high-resolution remote sensing (HRRS) images and classification of hyper-spectral remote sensing images.
RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data
In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsource data.
What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?
Is the DCNN recognition mechanism centered on object recognition still applicable to the scenarios of remote sensing scene understanding?
An adaptive Simulated Annealing-based satellite observation scheduling method combined with a dynamic task clustering strategy
In this study, we present an adaptive Simulated Annealing based scheduling algorithm aggregated with a dynamic task clustering strategy (or ASA-DTC for short) for satellite observation scheduling problems (SOSPs).
