Imagen de Portada

Mixture-of-Experts in Remote Sensing: A Survey

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Cui, Yongchuan, Liu, Peng, Chen, Lajiao
Formato:	Preprint
Publicado:	2026
Materias:	Computer Vision and Pattern Recognition
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting
por: Cui, Yongchuan, et al.
Publicado: (2026)

A Decade of Deep Learning for Remote Sensing Spatiotemporal Fusion: Advances, Challenges, and Opportunities
por: Sun, Enzhe, et al.
Publicado: (2025)

VFM-ISRefiner: Towards Better Adapting Vision Foundation Models for Interactive Segmentation of Remote Sensing Images
por: Wang, Deliang, et al.
Publicado: (2025)

Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need
por: Cui, Yongchuan, et al.
Publicado: (2025)

Leveraging Large-Scale Pretrained Spatial-Spectral Priors for General Zero-Shot Pansharpening
por: Cui, Yongchuan, et al.
Publicado: (2025)

UniRoute: Unified Routing Mixture-of-Experts for Modality-Adaptive Remote Sensing Change Detection
por: Shu, Qingling, et al.
Publicado: (2026)

Rethinking Efficient Mixture-of-Experts for Remote Sensing Modality-Missing Classification
por: Gao, Qinghao, et al.
Publicado: (2025)

Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution
por: Chen, Bowen, et al.
Publicado: (2025)

CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Experts
por: Hackel, Leonard, et al.
Publicado: (2025)

RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts
por: Liu, Xu, et al.
Publicado: (2024)

RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation
por: Bi, Hanbo, et al.
Publicado: (2025)

Hierarchical Fusion of Local and Global Visual Features with Mixture-of-Experts for Remote Sensing Image Scene Classification
por: Tang, Yuanhao, et al.
Publicado: (2025)

History-Augmented Contrastive Learning With Soft Mixture of Experts for Blind Super-Resolution of Planetary Remote Sensing Images
por: Zhao, Hui-Jia, et al.
Publicado: (2025)

Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey
por: Liu, Chenyang, et al.
Publicado: (2024)

Applications of Knowledge Distillation in Remote Sensing: A Survey
por: Himeur, Yassine, et al.
Publicado: (2024)

A Survey on Remote Sensing Foundation Models: From Vision to Multimodality
por: Huang, Ziyue, et al.
Publicado: (2025)

TwinDiffusion: Enhancing Coherence and Efficiency in Panoramic Image Generation with Diffusion Models
por: Zhou, Teng, et al.
Publicado: (2024)

MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models
por: Hanna, Joelle, et al.
Publicado: (2025)

Foundation Models for Remote Sensing and Earth Observation: A Survey
por: Xiao, Aoran, et al.
Publicado: (2024)

MIMRS: A Survey on Masked Image Modeling in Remote Sensing
por: Choudhury, Shabnam, et al.
Publicado: (2025)

RS-MoE: A Vision-Language Model with Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering
por: Lin, Hui, et al.
Publicado: (2024)

GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing
por: Xiao, Aoran, et al.
Publicado: (2026)

Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection
por: Cui, Yongchuan, et al.
Publicado: (2025)

RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
por: Liu, Fan, et al.
Publicado: (2023)

PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One Remote Sensing Image Restoration
por: Dong, Zhe, et al.
Publicado: (2025)

WMoE-CLIP: Wavelet-Enhanced Mixture-of-Experts Prompt Learning for Zero-Shot Anomaly Detection
por: Chen, Peng, et al.
Publicado: (2026)

Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook
por: Bao, Muyi, et al.
Publicado: (2025)

Aria: An Open Multimodal Native Mixture-of-Experts Model
por: Li, Dongxu, et al.
Publicado: (2024)

PanoLlama: Generating Endless and Coherent Panoramas with Next-Token-Prediction LLMs
por: Zhou, Teng, et al.
Publicado: (2024)

Mixture-of-Attack-Experts with Class Regularization for Unified Physical-Digital Face Attack Detection
por: Chen, Shunxin, et al.
Publicado: (2025)

Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications
por: Ndigande, Alain P., et al.
Publicado: (2026)

Multi-Task Dense Prediction via Mixture of Low-Rank Experts
por: Yang, Yuqi, et al.
Publicado: (2024)

A High-Level Survey of Optical Remote Sensing
por: Koletsis, Panagiotis, et al.
Publicado: (2026)

Vision Foundation Models in Remote Sensing: A Survey
por: Lu, Siqi, et al.
Publicado: (2024)

HIMOSA: Efficient Remote Sensing Image Super-Resolution with Hierarchical Mixture of Sparse Attention
por: Liu, Yi, et al.
Publicado: (2025)

RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image Classification
por: Zou, Guangwenjie, et al.
Publicado: (2024)

Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Survey
por: Lyu, Shuchang, et al.
Publicado: (2025)

Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey
por: Yu, Zihan, et al.
Publicado: (2024)

From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation
por: Liu, Quanwei, et al.
Publicado: (2025)

A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges
por: Ding, Lei, et al.
Publicado: (2025)