RSGen: Enhancing Layout-Driven Remote Sensing Image Generation with Diverse Edge Guidance

• URL: http://arxiv.org/abs/2603.15484v2
• Date: Tue, 17 Mar 2026 07:32:14 GMT
• Title: RSGen: Enhancing Layout-Driven Remote Sensing Image Generation with Diverse Edge Guidance
• Abstract summary: Diffusion models have significantly mitigated the impact of annotated data scarcity in remote sensing.<n>Recent approaches have successfully harnessed these models to enable diverse and controllable layout-to-Image synthesis.<n>We propose RSGen, a plug-and-play framework that leverages diverse edge guidance to enhance layout-driven RS image generation.
• Score: 15.916510585915406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have significantly mitigated the impact of annotated data scarcity in remote sensing (RS). Although recent approaches have successfully harnessed these models to enable diverse and controllable Layout-to-Image (L2I) synthesis, they still suffer from limited fine-grained control and fail to strictly adhere to bounding box constraints. To address these limitations, we propose RSGen, a plug-and-play framework that leverages diverse edge guidance to enhance layout-driven RS image generation. Specifically, RSGen employs a progressive enhancement strategy: 1) it first enriches the diversity of edge maps composited from retrieved training instances via Image-to-Image generation; and 2) subsequently utilizes these diverse edge maps as conditioning for existing L2I models to enforce pixel-level control within bounding boxes, ensuring the generated instances strictly adhere to the layout. Extensive experiments across three baseline models demonstrate that RSGen significantly boosts the capabilities of existing L2I models. For instance, with CC-Diff on the DOTA dataset for oriented object detection, we achieve remarkable gains of +9.8/+12.0 in YOLOScore mAP50/mAP50-95 and +1.6 in mAP on the downstream detection task. Our code will be publicly available: https://github.com/D-Robotics-AI-Lab/RSGen

Related papers

• SFFNet: Synergistic Feature Fusion Network With Dual-Domain Edge Enhancement for UAV Image Object Detection [25.963089047314963]
  Object detection in unmanned aerial vehicle (UAV) images remains a highly challenging task.<n>We have developed a synergistic feature fusion network (SFFNet) with dual-domain edge enhancement specifically tailored for object detection in UAV images.<n> Experiments on two challenging aerial datasets demonstrate the outstanding performance of SFFNet-X.
  arXiv  Detail & Related papers  (2026-04-03T16:52:49Z)
• Training-Free Layout-to-Image Generation with Marginal Attention Constraints [73.55660250459132]
  We propose a training-free layout-to-image (L2I) approach, which eliminates the need for additional modules or fine-tuning.<n>Specifically, we use text-visual cross-attention feature maps to quantify inconsistencies between the layout of the generated images and the provided instructions.<n>We leverage pixel-to-pixel correlations in the self-attention feature maps to align cross-attention maps and combine three loss functions constrained by boundary attention to update latent features.
  arXiv  Detail & Related papers  (2024-11-15T05:44:45Z)
• LiOn-XA: Unsupervised Domain Adaptation via LiDAR-Only Cross-Modal Adversarial Training [61.26381389532653]
  LiOn-XA is an unsupervised domain adaptation (UDA) approach that combines LiDAR-Only Cross-Modal (X) learning with Adversarial training for 3D LiDAR point cloud semantic segmentation. Our experiments on 3 real-to-real adaptation scenarios demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-10-21T09:50:17Z)
• ControlSR: Taming Diffusion Models for Consistent Real-World Image Super Resolution [68.72454974431749]
  We present ControlSR, a new method that can tame Diffusion Models for consistent real-world image super-resolution (Real-ISR)<n>Our model can achieve better performance across multiple metrics on several test sets and generate more consistent SR results with LR images than existing methods.
  arXiv  Detail & Related papers  (2024-10-18T08:35:57Z)
• CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model [42.92146478120197]
  CRS-Diff is a new RS generative framework specifically tailored for RS image generation. To our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks.
  arXiv  Detail & Related papers  (2024-03-18T09:44:44Z)
• DivCon: Divide and Conquer for Complex Numerical and Spatial Reasoning in Text-to-Image Generation [0.0]
  Diffusion-driven text-to-image (T2I) generation has achieved remarkable advancements in recent years.<n> layout is employed as an intermedium to bridge large language models and layout-based diffusion models.<n>We introduce a divide-and-conquer approach which decouples the generation task into multiple subtasks.
  arXiv  Detail & Related papers  (2024-03-11T03:24:44Z)
• Gen2Det: Generate to Detect [42.13657805295144]
  We motivate and present Gen2Det, a simple modular pipeline to create synthetic training data for object detection for free. In addition to the synthetic data, Gen2Det proposes a suite of techniques to best utilize the generated data, including image-level filtering, instance-level filtering, and better training recipe.
  arXiv  Detail & Related papers  (2023-12-07T18:59:58Z)
• GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation [91.01581867841894]
  We propose the GeoDiffusion, a simple framework that can flexibly translate various geometric conditions into text prompts. Our GeoDiffusion is able to encode not only the bounding boxes but also extra geometric conditions such as camera views in self-driving scenes.
  arXiv  Detail & Related papers  (2023-06-07T17:17:58Z)
• RGB-D Saliency Detection via Cascaded Mutual Information Minimization [122.8879596830581]
  Existing RGB-D saliency detection models do not explicitly encourage RGB and depth to achieve effective multi-modal learning. We introduce a novel multi-stage cascaded learning framework via mutual information minimization to "explicitly" model the multi-modal information between RGB image and depth data.
  arXiv  Detail & Related papers  (2021-09-15T12:31:27Z)
• Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization [59.19214040221055]
  We propose a novel spatial-separated curve rendering network (S$2$CRNet) for efficient and high-resolution image harmonization. The proposed method reduces more than 90% parameters compared with previous methods. Our method can work smoothly on higher resolution images in real-time which is more than 10$times$ faster than the existing methods.
  arXiv  Detail & Related papers  (2021-09-13T07:20:16Z)

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.