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Found 45 papers, 20 papers with code
BRepFormer: Transformer-Based B-rep Geometric Feature Recognition
BRepFormer encodes and fuses the geometric and topological features of the models.
UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report Generation
Automated radiology report generation aims to expedite the tedious and error-prone reporting process for radiologists.
CausalCLIPSeg: Unlocking CLIP's Potential in Referring Medical Image Segmentation with Causal Intervention
Inspired by large-scale pre-trained vision-language models, we propose CausalCLIPSeg, an end-to-end framework for referring medical image segmentation that leverages CLIP.
Reducing Annotation Burden: Exploiting Image Knowledge for Few-Shot Medical Video Object Segmentation via Spatiotemporal Consistency Relearning
Our model bridges the gap between abundant annotated medical images and scarce, sparsely labeled medical videos to achieve strong video segmentation performance in this low data regime.
One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks
Video object segmentation is crucial for the efficient analysis of complex medical video data, yet it faces significant challenges in data availability and annotation.
Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models
Ultrasound video classification enables automated diagnosis and has emerged as an important research area.
Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection
Following this paradigm, we propose a novel scale-aware contrastive reverse distillation model that addresses two key limitations of existing reverse distillation methods: insufficient feature discriminability and inability to handle anomaly scale variations.
Rethinking Cell Counting Methods: Decoupling Counting and Localization
Cell counting in microscopy images is vital in medicine and biology but extremely tedious and time-consuming to perform manually.
Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation
Medical ultrasound imaging is ubiquitous, but manual analysis struggles to keep pace.
Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures.
Physics-embedded Fourier Neural Network for Partial Differential Equations
The MC-Fourier layer is by design translation- and rotation-invariant in the frequency domain, serving as a plug-and-play module that adheres to the laws of momentum conservation.
On the Foundations of Earth and Climate Foundation Models
Foundation models have enormous potential in advancing Earth and climate sciences, however, current approaches may not be optimal as they focus on a few basic features of a desirable Earth and climate foundation model.
GlobalBuildingMap -- Unveiling the Mystery of Global Buildings
A joint analysis of building maps and solar potentials indicates that rooftop solar energy can supply the global energy consumption need at a reasonable cost.
Large-scale flood modeling and forecasting with FloodCast
Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost.
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
Specifically, we present a comprehensive review of the physics-aware ML methods, building a structured community (PaML) of existing methodologies that integrate prior physical knowledge or physics-based modeling into ML.
HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion
Deep neural networks based on unrolled iterative algorithms have achieved remarkable success in sparse reconstruction applications, such as synthetic aperture radar (SAR) tomographic inversion (TomoSAR).
HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images
To tackle this problem, we propose a method for monocular height estimation from optical imagery, which is currently one of the richest sources of remote sensing data.
Self-supervised Domain-agnostic Domain Adaptation for Satellite Images
A promising method to address this problem is domain adaptation, where the training and the testing datasets are split into two or multiple domains according to their distributions, and an adaptation method is applied to improve the generalizability of the model on the testing dataset.
Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects
In this context, few-shot object detection (FSOD) has emerged as a promising direction, which aims at enabling the model to detect novel objects with only few of them annotated.
UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation
Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency.
PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds
We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds.
DisasterNets: Embedding Machine Learning in Disaster Mapping
It consists of two stages, space granulation and attribute granulation.
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography
An emerging technique known as deep unrolling provided a good combination of the descriptive ability of neural networks, explainable, and computational efficiency for BPDN.
Building Footprint Extraction with Graph Convolutional Network
In this work, we have proposed a end-to-end framework to overcome this issue, which uses the graph convolutional network (GCN) for building footprint extraction task.
High Quality Large-Scale 3-D Urban Mapping with Multi-Master TomoSAR
Multi-baseline interferometric synthetic aperture radar (InSAR) techniques are effective approaches for retrieving the 3-D information of urban areas.
MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer
With the increasing demand for video understanding, video moment and highlight detection (MHD) has emerged as a critical research topic.
Universal Domain Adaptation for Remote Sensing Image Scene Classification
Empirical results show that the proposed model is effective and practical for remote sensing image scene classification, regardless of whether the source data is available or not.
Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark
With this dataset, we consider the problem of cloud removal in high-resolution optical remote sensing imagery by integrating multi-modal and multi-resolution information.
EarthNets: Empowering AI in Earth Observation
Furthermore, a new platform for EO, termed EarthNets, is released to achieve a fair and consistent evaluation of deep learning methods on remote sensing data.
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally.
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover.
Ranked #3 on
Cloud Removal
on SEN12MS-CR
Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training
Considering that rich information is also encoded in feature maps, we propose to integrate the consistency of both features and outputs in the end-to-end network training of unlabeled samples, enabling to impose additional constraints.
Disentangled Latent Transformer for Interpretable Monocular Height Estimation
Furthermore, a novel unsupervised semantic segmentation task based on height estimation is first introduced in this work.
$\boldsymbolγ$-Net: Superresolving SAR Tomographic Inversion via Deep Learning
This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers.
Lebanon Solar Rooftop Potential Assessment using Buildings Segmentation from Aerial Images
Factors such as size, ground coverage ratio and PV_out are carefully investigated for each district.
Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images
To address this issue, we propose an adversarial shape learning network (ASLNet) to model the building shape patterns that improve the accuracy of building segmentation.
Exploring Deep 3D Spatial Encodings for Large-Scale 3D Scene Understanding
In this letter, we have proposed an alternative approach to overcome the limitations of CNN based approaches by encoding the spatial features of raw 3D point clouds into undirected symmetrical graph models.
Deep Learning Meets SAR
Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data.
Instance segmentation of buildings using keypoints
The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building.
SAR Tomography at the Limit: Building Height Reconstruction Using Only 3-5 TanDEM-X Bistatic Interferograms
Multi-baseline interferometric synthetic aperture radar (InSAR) techniques are effective approaches for retrieving the 3-D information of urban areas.
Building Footprint Generation by IntegratingConvolution Neural Network with Feature PairwiseConditional Random Field (FPCRF)
Due to the complexity of buildings, the accurate and reliable generation of the building footprint from remote sensing imagery is still a challenging task.
So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification
This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-the-art machine learning techniques.
BIG-bench Machine Learning
Cultural Vocal Bursts Intensity Prediction
+1
Building Segmentation through a Gated Graph Convolutional Neural Network with Deep Structured Feature Embedding
Taking the semantic segmentation of building footprints as a practical example, we compared different feature embedding architectures and graph neural networks.
Non-Local Compressive Sensing Based SAR Tomography
We propose to increase SNR by integrating non-local estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible.
Building Footprint Generation Using Improved Generative Adversarial Networks
The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes.
