Search Results for author:
Found 74 papers, 42 papers with code
MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting
Deep learning approaches for marine fog detection and forecasting have outperformed traditional methods, demonstrating significant scientific and practical importance.
BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via Adaptive Block-Based Gaussian Splatting
This paper introduces BlockGaussian, a novel framework incorporating a content-aware scene partition strategy and visibility-aware block optimization to achieve efficient and high-quality large-scale scene reconstruction.
Towards Computation- and Communication-efficient Computational Pathology
Despite the impressive performance across a wide range of applications, current computational pathology models face significant diagnostic efficiency challenges due to their reliance on high-magnification whole-slide image analysis.
DynamicVis: An Efficient and General Visual Foundation Model for Remote Sensing Image Understanding
Motivated by the selective attention mechanisms inherent to the human visual system, we propose DynamicVis, a dynamic visual perception foundation model for remote sensing imagery.
Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution
To address this issue, we introduce a Mixture of Experts (MoE) model and design a set of heterogeneous experts.
Kolmogorov Arnold Neural Interpolator for Downscaling and Correcting Meteorological Fields from In-Situ Observations
Obtaining accurate weather forecasts at station locations is a critical challenge due to systematic biases arising from the mismatch between multi-scale, continuous atmospheric characteristic and their discrete, gridded representations.
WSSM: Geographic-enhanced hierarchical state-space model for global station weather forecast
Global Station Weather Forecasting (GSWF), a prominent meteorological research area, is pivotal in providing timely localized weather predictions.
Semantic-CD: Remote Sensing Image Semantic Change Detection towards Open-vocabulary Setting
To address this issue, we introduce a novel approach called Semantic-CD, specifically designed for semantic change detection in remote sensing images.
RSRefSeg: Referring Remote Sensing Image Segmentation with Foundation Models
Referring remote sensing image segmentation is crucial for achieving fine-grained visual understanding through free-format textual input, enabling enhanced scene and object extraction in remote sensing applications.
Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model
On the previous benchmark dataset, Text2Earth outperforms previous models with an improvement of +26. 23 FID and +20. 95% Zero-shot Cls-OA metric. Our project page is \url{https://chen-yang-liu. github. io/Text2Earth}
Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation
In this paper, we propose a novel semantic splatting approach based on Gaussian Splatting to achieve efficient and low-latency.
Remote Sensing Temporal Vision-Language Models: A Comprehensive Survey
This survey fills a critical gap in the literature by providing an integrated overview of RSTVLM, offering a foundation for further advancements in remote sensing temporal image understanding.
Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model
This task presents two principal challenges: (1) severe inconsistencies in feature representation across different remote sensing domains, and (2) a domain gap that emerges due to the representation bias of source domain patterns when translating features to predictive logits.
Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes
Recent advancements in human motion synthesis have focused on specific types of motions, such as human-scene interaction, locomotion or human-human interaction, however, there is a lack of a unified system capable of generating a diverse combination of motion types.
Exploring Fine-Grained Image-Text Alignment for Referring Remote Sensing Image Segmentation
Given a language expression, referring remote sensing image segmentation (RRSIS) aims to identify ground objects and assign pixel-wise labels within the imagery.
Adapt CLIP as Aggregation Instructor for Image Dehazing
Most dehazing methods suffer from limited receptive field and do not explore the rich semantic prior encapsulated in vision-language models, which have proven effective in downstream tasks.
MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling
In an era of frequent extreme weather and global warming, obtaining precise, fine-grained near-surface weather forecasts is increasingly essential for human activities.
Open-CD: A Comprehensive Toolbox for Change Detection
We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules.
Learning at a Glance: Towards Interpretable Data-limited Continual Semantic Segmentation via Semantic-Invariance Modelling
Concretely, the proposed decoupling manner includes two ways, i. e., channel-wise decoupling and spatial-level neuron-relevant semantic consistency.
Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance
Remote sensing image change captioning (RSICC) aims to articulate the changes in objects of interest within bi-temporal remote sensing images using natural language.
Transformer for Multitemporal Hyperspectral Image Unmixing
To effectively perform multitemporal hyperspectral image unmixing, we introduce two key modules: the Global Awareness Module (GAM) and the Change Enhancement Module (CEM).
C$^3$DG: Conditional Domain Generalization for Hyperspectral Imagery Classification with Convergence and Constrained-risk Theories
In this paper, we only rely on spectral information to solve the hyperspectral-monospectra problem, and propose a Convergence and Error-Constrained Conditional Domain Generalization method for Hyperspectral Imagery Classification (C$^3$DG).
M4Fog: A Global Multi-Regional, Multi-Modal, and Multi-Stage Dataset for Marine Fog Detection and Forecasting to Bridge Ocean and Atmosphere
Marine fog poses a significant hazard to global shipping, necessitating effective detection and forecasting to reduce economic losses.
A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection
Recently some multi-task learning based semantic change detection methods have been proposed to decompose the task into semantic segmentation and binary change detection subtasks.
Domain Agnostic Conditional Invariant Predictions for Domain Generalization
In DRM theory, we prove that reducing the discrepancy of prediction distribution between overall source domain and any subset of it can contribute to obtaining invariant features.
Domain Generalization Guided by Large-Scale Pre-Trained Priors
In summary, we offer a new fine-tuning method for DG algorithms to utilize pre-trained models throughout the fine-tuning process.
CDMamba: Remote Sensing Image Change Detection with Mamba
Specifically, the Scaled Residual ConvMamba (SRCM) block is proposed to utilize the ability of Mamba to extract global features and convolution to enhance the local details, to alleviate the issue that current Mamba-based methods lack detailed clues and are difficult to achieve fine detection in dense prediction tasks.
Ranked #5 on
Change Detection
on WHU-CD
MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation
To achieve unbounded and arbitrary-sized image generation, we design a novel noise sampling strategy for denoising diffusion models by analyzing the generation conditions and initial noise.
RSCaMa: Remote Sensing Image Change Captioning with State Space Model
In terms of efficient temporal modeling, considering the potential correlation between the temporal scanning characteristics of Mamba and the temporality of the RSICC, we propose the Temporal-Traversing SSM (TT-SSM), which scans bi-temporal features in a temporal cross-wise manner, enhancing the model's temporal understanding and information interaction.
MarsSeg: Mars Surface Semantic Segmentation with Multi-level Extractor and Connector
The Mini-ASPP and PSA are specifically designed for shadow feature enhancement, thereby enabling the expression of local details and small objects.
Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis
Remote sensing satellite imagery offers a unique perspective for monitoring these changes, leading to the emergence of remote sensing image change interpretation (RSICI) as a significant research focus.
RSMamba: Remote Sensing Image Classification with State Space Model
Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation.
BiFA: Remote Sensing Image Change Detection With Bitemporal Feature Alignment
Despite the success of deep learning-based change detection (CD) methods, their existing insufficiency in temporal (channel and spatial) and multiscale alignment has rendered them insufficient capability in mitigating external factors (illumination changes and perspective differences) arising from different imaging conditions during CD.
Ranked #2 on
Change Detection
on WHU-CD
Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method
In this paper, we extend meteorological downscaling to arbitrary scattered station scales, establish a brand new benchmark and dataset, and retrieve meteorological states at any given station location from a coarse-resolution meteorological field.
Learning to detect cloud and snow in remote sensing images from noisy labels
This paper is the first to consider the impact of label noise on the detection of clouds and snow in remote sensing images.
DeepPhysiNet: Bridging Deep Learning and Atmospheric Physics for Accurate and Continuous Weather Modeling
Next, we construct hyper-networks based on deep learning methods to directly learn weather patterns from a large amount of meteorological data.
Pixel-Level Change Detection Pseudo-Label Learning for Remote Sensing Change Captioning
The existing methods for Remote Sensing Image Change Captioning (RSICC) perform well in simple scenes but exhibit poorer performance in complex scenes.
Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change Detection
Change detection, a prominent research area in remote sensing, is pivotal in observing and analyzing surface transformations.
Ranked #11 on
Change Detection
on LEVIR-CD
PETDet: Proposal Enhancement for Two-Stage Fine-Grained Object Detection
Fine-grained object detection (FGOD) extends object detection with the capability of fine-grained recognition.
Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions
We first propose a theorem to show that the invariant posterior of parameters can be implicitly inferred by aggregating posteriors on different training domains.
Be Bayesian by Attachments to Catch More Uncertainty
Bayesian Neural Networks (BNNs) have become one of the promising approaches for uncertainty estimation due to the solid theorical foundations.
Inherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory
In this paper, we propose to address CISS without exemplar memory and resolve catastrophic forgetting as well as semantic drift synchronously.
Class-Incremental Semantic Segmentation
Contrastive Learning
+2
Zero-shot Text-driven Physically Interpretable Face Editing
Our method can also be flexibly extended to real-time video face editing.
Zero-Shot Image Harmonization with Generative Model Prior
Initially, we employ a pretrained vision-language model (VLM) to generate descriptions for the composite image.
Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation
And the feature diversity is preserved by inter- and intra- class feature diversity-preserved module (InCDP).
RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
We also propose several ongoing derivatives for instance segmentation tasks, drawing on recent advancements within the SAM community, and compare their performance with RSPrompter.
Continuous Cross-resolution Remote Sensing Image Change Detection
Toward continuous cross-resolution CD, we propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying resolution differences.
Diffusion Models for Imperceptible and Transferable Adversarial Attack
Many existing adversarial attacks generate $L_p$-norm perturbations on image RGB space.
Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation
In the first stage, we optimize a neural field to encode the color and 3D structure of the remote sensing scene based on multi-view images.
Dense Pixel-to-Pixel Harmonization via Continuous Image Representation
High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing.
Ranked #8 on
Image Harmonization
on iHarmony4
Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation
In our method, taking the power of large-scale pre-trained multi-modal CLIP and neural rendering, T2P searches both continuous facial parameters and discrete facial parameters in a unified framework.
Progressive Scale-aware Network for Remote sensing Image Change Captioning
To sufficiently utilize the extracted multi-scale features for captioning, we propose a scale-aware reinforcement (SR) module and combine it with the Transformer decoding layer to progressively utilize the features from different PDP layers.
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification
In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication.
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space
Despite its fruitful applications in remote sensing, image super-resolution is troublesome to train and deploy as it handles different resolution magnifications with separate models.
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation
To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder.
Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection
To achieve this, we obtain multiple points via class-balanced sampling on the overlapped area between views using the semantic mask.
Remote Sensing Novel View Synthesis with Implicit Multiplane Representations
In this paper, we propose a novel remote sensing view synthesis method by leveraging the recent advances in implicit neural representations.
HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images
In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast
Tropical cyclone (TC) is an extreme tropical weather system and its trajectory can be described by a variety of spatio-temporal data.
Semantic decoupled representation learning for remote sensing image change detection
Here, we propose a semantic decoupled representation learning for RS image CD.
Building Extraction from Remote Sensing Images with Sparse Token Transformers
Deep learning methods have achieved considerable progress in remote sensing image building extraction.
Building change detection for remote sensing images
Extracting Buildings In Remote Sensing Images
PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning
Histopathological tissue classification is a fundamental task in pathomics cancer research.
Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels
In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision.
Geographical Knowledge-driven Representation Learning for Remote Sensing Images
The proliferation of remote sensing satellites has resulted in a massive amount of remote sensing images.
Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics
In this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem in order to better reflect the diagnosis process in the clinical setting and for the higher interpretability of the output.
Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images
The key of IAug is to blend synthesized building instances onto appropriate positions of one of the bitemporal images.
Ranked #34 on
Building change detection for remote sensing images
on LEVIR-CD
(using extra training data)
Building change detection for remote sensing images
Change Detection
+1
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery
To be specific, we develop a new Feedback Structure and a Local-Global Spectral Block to alleviate the difficulty in spatial and spectral feature extraction.
Remote Sensing Image Change Detection with Transformers
To achieve this, we express the bitemporal image into a few tokens, and use a transformer encoder to model contexts in the compact token-based space-time.
Building change detection for remote sensing images
Change Detection
NeuralMagicEye: Learning to See and Understand the Scene Behind an Autostereogram
This paper studies an interesting question that whether a deep CNN can be trained to recover the depth behind an autostereogram and understand its content.
Stylized Neural Painting
Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering.
A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection
Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images.
Face-to-Parameter Translation for Game Character Auto-Creation
Character customization system is an important component in Role-Playing Games (RPGs), where players are allowed to edit the facial appearance of their in-game characters with their own preferences rather than using default templates.
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years.
