RingMo-Agent: A Unified Remote Sensing Foundation Model for Multi-Platform and Multi-Modal Reasoning

• URL: http://arxiv.org/abs/2507.20776v1
• Date: Mon, 28 Jul 2025 12:39:33 GMT
• Title: RingMo-Agent: A Unified Remote Sensing Foundation Model for Multi-Platform and Multi-Modal Reasoning
• Authors: Huiyang Hu, Peijin Wang, Yingchao Feng, Kaiwen Wei, Wenxin Yin, Wenhui Diao, Mengyu Wang, Hanbo Bi, Kaiyue Kang, Tong Ling, Kun Fu, Xian Sun,
• Abstract summary: RingMo-Agent is designed to handle multi-modal and multi-platform data.<n>It is supported by a large-scale vision-language dataset named RS-VL3M.<n>It proves effective in both visual understanding and sophisticated analytical tasks.
• Score: 15.670921552151775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote sensing (RS) images from multiple modalities and platforms exhibit diverse details due to differences in sensor characteristics and imaging perspectives. Existing vision-language research in RS largely relies on relatively homogeneous data sources. Moreover, they still remain limited to conventional visual perception tasks such as classification or captioning. As a result, these methods fail to serve as a unified and standalone framework capable of effectively handling RS imagery from diverse sources in real-world applications. To address these issues, we propose RingMo-Agent, a model designed to handle multi-modal and multi-platform data that performs perception and reasoning tasks based on user textual instructions. Compared with existing models, RingMo-Agent 1) is supported by a large-scale vision-language dataset named RS-VL3M, comprising over 3 million image-text pairs, spanning optical, SAR, and infrared (IR) modalities collected from both satellite and UAV platforms, covering perception and challenging reasoning tasks; 2) learns modality adaptive representations by incorporating separated embedding layers to construct isolated features for heterogeneous modalities and reduce cross-modal interference; 3) unifies task modeling by introducing task-specific tokens and employing a token-based high-dimensional hidden state decoding mechanism designed for long-horizon spatial tasks. Extensive experiments on various RS vision-language tasks demonstrate that RingMo-Agent not only proves effective in both visual understanding and sophisticated analytical tasks, but also exhibits strong generalizability across different platforms and sensing modalities.

Related papers

• VisualTrans: A Benchmark for Real-World Visual Transformation Reasoning [10.497961559068493]
  Visual transformation reasoning (VTR) is a vital cognitive capability that empowers intelligent agents to understand dynamic scenes.<n>Existing benchmarks suffer from a sim-to-real gap, limited task complexity, and incomplete reasoning coverage.<n>VisualTrans is the first comprehensive benchmark specifically designed for VTR in real-world human-object interaction scenarios.
  arXiv  Detail & Related papers  (2025-08-06T03:07:05Z)
• MGCR-Net:Multimodal Graph-Conditioned Vision-Language Reconstruction Network for Remote Sensing Change Detection [55.702662643521265]
  We propose the multimodal graph-conditioned vision-language reconstruction network (MGCR-Net) to explore the semantic interaction capabilities of multimodal data.<n> Experimental results on four public datasets demonstrate that MGCR achieves superior performance compared to mainstream CD methods.
  arXiv  Detail & Related papers  (2025-08-03T02:50:08Z)
• MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings [75.0617088717528]
  MoCa is a framework for transforming pre-trained VLM backbones into effective bidirectional embedding models.<n>MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results.
  arXiv  Detail & Related papers  (2025-06-29T06:41:00Z)
• AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection [58.67129770371016]
  We propose a novel IRSTD framework that reimagines the IRSTD paradigm by incorporating textual metadata for scene-aware optimization.<n>AuxDet consistently outperforms state-of-the-art methods, validating the critical role of auxiliary information in improving robustness and accuracy.
  arXiv  Detail & Related papers  (2025-05-21T07:02:05Z)
• Multimodal-Aware Fusion Network for Referring Remote Sensing Image Segmentation [7.992331117310217]
  Referring remote sensing image segmentation (RRSIS) is a novel visual task in remote sensing images segmentation.<n>We design a multimodal-aware fusion network (MAFN) to achieve fine-grained alignment and fusion between the two modalities.
  arXiv  Detail & Related papers  (2025-03-14T08:31:21Z)
• UniRS: Unifying Multi-temporal Remote Sensing Tasks through Vision Language Models [23.044366104080822]
  We introduce textbfUniRS, the first vision-language model bftextremote bftextsensing tasks across various types of visual input.<n>UniRS supports single images, dual-time image pairs, and videos as input, enabling comprehensive remote sensing temporal analysis.<n> Experimental results show that UniRS achieves state-of-the-art performance across diverse tasks.
  arXiv  Detail & Related papers  (2024-12-30T06:34:18Z)
• 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)
• RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts [17.76606110070648]
  We propose RSUniVLM, a unified, end-to-end RS VLM for comprehensive vision understanding across multiple granularity.<n> RSUniVLM performs effectively in multi-image analysis, with instances of change detection and change captioning.<n>We also construct a large-scale RS instruction-following dataset based on a variety of existing datasets in both RS and general domain.
  arXiv  Detail & Related papers  (2024-12-07T15:11:21Z)
• X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning [109.9413329636322]
  This paper introduces an efficient framework that integrates multiple modalities (images, 3D, audio and video) to a frozen Large Language Models (LLMs) Our approach explores two distinct projection mechanisms: Q-Formers and Linear Projections (LPs)
  arXiv  Detail & Related papers  (2023-11-30T18:43:51Z)
• UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning [86.91893533388628]
  This paper presents UniDiff, a unified multi-modal model that integrates image-text contrastive learning (ITC), text-conditioned image synthesis learning (IS), and reciprocal semantic consistency modeling (RSC) UniDiff demonstrates versatility in both multi-modal understanding and generative tasks.
  arXiv  Detail & Related papers  (2023-06-01T15:39:38Z)
• Support-set based Multi-modal Representation Enhancement for Video Captioning [121.70886789958799]
  We propose a Support-set based Multi-modal Representation Enhancement (SMRE) model to mine rich information in a semantic subspace shared between samples. Specifically, we propose a Support-set Construction (SC) module to construct a support-set to learn underlying connections between samples and obtain semantic-related visual elements. During this process, we design a Semantic Space Transformation (SST) module to constrain relative distance and administrate multi-modal interactions in a self-supervised way.
  arXiv  Detail & Related papers  (2022-05-19T03:40:29Z)

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.