RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation

• URL: http://arxiv.org/abs/2504.03166v1
• Date: Fri, 04 Apr 2025 04:47:54 GMT
• Title: RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation
• Authors: Hanbo Bi, Yingchao Feng, Boyuan Tong, Mengyu Wang, Haichen Yu, Yongqiang Mao, Hao Chang, Wenhui Diao, Peijin Wang, Yue Yu, Hanyang Peng, Yehong Zhang, Kun Fu, Xian Sun,
• Abstract summary: RingMoE is a unified RS foundation model with 14.7 billion parameters, pre-trained on 400 million multi-modal RS images from nine satellites.<n>It has been deployed and trialed in multiple sectors, including emergency response, land management, marine sciences, and urban planning.
• Score: 24.48561340129571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of foundation models has revolutionized visual representation learning in a self-supervised manner. However, their application in remote sensing (RS) remains constrained by a fundamental gap: existing models predominantly handle single or limited modalities, overlooking the inherently multi-modal nature of RS observations. Optical, synthetic aperture radar (SAR), and multi-spectral data offer complementary insights that significantly reduce the inherent ambiguity and uncertainty in single-source analysis. To bridge this gap, we introduce RingMoE, a unified multi-modal RS foundation model with 14.7 billion parameters, pre-trained on 400 million multi-modal RS images from nine satellites. RingMoE incorporates three key innovations: (1) A hierarchical Mixture-of-Experts (MoE) architecture comprising modal-specialized, collaborative, and shared experts, effectively modeling intra-modal knowledge while capturing cross-modal dependencies to mitigate conflicts between modal representations; (2) Physics-informed self-supervised learning, explicitly embedding sensor-specific radiometric characteristics into the pre-training objectives; (3) Dynamic expert pruning, enabling adaptive model compression from 14.7B to 1B parameters while maintaining performance, facilitating efficient deployment in Earth observation applications. Evaluated across 23 benchmarks spanning six key RS tasks (i.e., classification, detection, segmentation, tracking, change detection, and depth estimation), RingMoE outperforms existing foundation models and sets new SOTAs, demonstrating remarkable adaptability from single-modal to multi-modal scenarios. Beyond theoretical progress, it has been deployed and trialed in multiple sectors, including emergency response, land management, marine sciences, and urban planning.

Related papers

• Rethinking Multi-modal Object Detection from the Perspective of Mono-Modality Feature Learning [18.268054258939213]
  We introduce linear probing evaluation to the multi-modal detectors and rethink the multi-modal object detection task.<n>We construct an novel framework called M$2$D-LIF, which consists of the Mono-Modality Distillation (M$2$D) method and the Local Illumination-aware Fusion (LIF) module.<n>Our M$2$D-LIF effectively mitigates the Fusion Degradation phenomenon and outperforms the previous SOTA detectors.
  arXiv  Detail & Related papers  (2025-03-14T18:15:53Z)
• Zero-Shot Interactive Text-to-Image Retrieval via Diffusion-Augmented Representations [7.439049772394586]
  Diffusion Augmented Retrieval (DAR) is a paradigm-shifting framework that bypasses MLLM finetuning entirely.<n>DAR synergizes Large Language Model (LLM)-guided query refinement with Diffusion Model (DM)-based visual synthesis to create contextually enriched intermediate representations.
  arXiv  Detail & Related papers  (2025-01-26T03:29:18Z)
• SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection [73.49799596304418]
  This paper introduces a new task called Multi-Modal datasets and Multi-Task Object Detection (M2Det) for remote sensing.<n>It is designed to accurately detect horizontal or oriented objects from any sensor modality.<n>This task poses challenges due to 1) the trade-offs involved in managing multi-modal modelling and 2) the complexities of multi-task optimization.
  arXiv  Detail & Related papers  (2024-12-30T02:47:51Z)
• Exploring Missing Modality in Multimodal Egocentric Datasets [89.76463983679058]
  We introduce a novel concept -Missing Modality Token (MMT)-to maintain performance even when modalities are absent. Our method mitigates the performance loss, reducing it from its original $sim 30%$ drop to only $sim 10%$ when half of the test set is modal-incomplete.
  arXiv  Detail & Related papers  (2024-01-21T11:55:42Z)
• SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery [35.550999964460466]
  We present SkySense, a generic billion-scale model, pre-trained on a curated multi-modal Remote Sensing dataset with 21.5 million temporal sequences. To our best knowledge, SkySense is the largest Multi-Modal to date, whose modules can be flexibly combined or used individually to accommodate various tasks.
  arXiv  Detail & Related papers  (2023-12-15T09:57:21Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities [76.08541852988536]
  We propose to use invariant features for a missing modality imagination network (IF-MMIN) We show that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions.
  arXiv  Detail & Related papers  (2022-10-27T12:16:25Z)
• Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis [96.46952672172021]
  Bi-Bimodal Fusion Network (BBFN) is a novel end-to-end network that performs fusion on pairwise modality representations. Model takes two bimodal pairs as input due to known information imbalance among modalities.
  arXiv  Detail & Related papers  (2021-07-28T23:33:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.