Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning

• URL: http://arxiv.org/abs/2412.19179v2
• Date: Sun, 16 Feb 2025 09:13:25 GMT
• Title: Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning
• Authors: Dongwei Sun, Jing Yao, Changsheng Zhou, Xiangyong Cao, Pedram Ghamisi,
• Abstract summary: This paper proposes a novel approach for remote sensing image change detection and description that incorporates diffusion models. We introduce a frequency-guided complex filter module to boost the model performance by managing high-frequency noise. We validate the effectiveness of our proposed method across several datasets for remote sensing change detection and description.
• Score: 15.88864190284027
• License:
• Abstract: Remote sensing image change description represents an innovative multimodal task within the realm of remote sensing processing. This task not only facilitates the detection of alterations in surface conditions, but also provides comprehensive descriptions of these changes, thereby improving human interpretability and interactivity.Generally, existing deep-learning-based methods predominantly utilized a three-stage framework that successively perform feature extraction, feature fusion, and localization from bitemporal images before text generation. However, this reliance often leads to an excessive focus on the design of specific network architectures and restricts the feature distributions to the dataset at hand, which in turn results in limited generalizability and robustness during application.To address these limitations, this paper proposes a novel approach for remote sensing image change detection and description that incorporates diffusion models, aiming to transition the emphasis of modeling paradigms from conventional feature learning to data distribution learning. The proposed method primarily includes a simple multi-scale change detection module, whose output features are subsequently refined by an well-designed diffusion model. Furthermore, we introduce a frequency-guided complex filter module to boost the model performance by managing high-frequency noise throughout the diffusion process. We validate the effectiveness of our proposed method across several datasets for remote sensing change detection and description, showcasing its superior performance compared to existing techniques. The code will be available at \href{https://github.com/sundongwei}{MaskApproxNet} after a possible publication.

Related papers

• Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation [54.96563068182733]
  We propose Modality Adaptation with text-to-image Diffusion Models (MADM) for semantic segmentation task. MADM utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model's cross-modality capabilities. We show that MADM achieves state-of-the-art adaptation performance across various modality tasks, including images to depth, infrared, and event modalities.
  arXiv  Detail & Related papers  (2024-10-29T03:49:40Z)
• Effective Diffusion Transformer Architecture for Image Super-Resolution [63.254644431016345]
  We design an effective diffusion transformer for image super-resolution (DiT-SR) In practice, DiT-SR leverages an overall U-shaped architecture, and adopts a uniform isotropic design for all the transformer blocks. We analyze the limitation of the widely used AdaLN, and present a frequency-adaptive time-step conditioning module.
  arXiv  Detail & Related papers  (2024-09-29T07:14:16Z)
• Novel Change Detection Framework in Remote Sensing Imagery Using Diffusion Models and Structural Similarity Index (SSIM) [0.0]
  Change detection is a crucial task in remote sensing, enabling the monitoring of environmental changes, urban growth, and disaster impact. Recent advancements in machine learning, particularly generative models like diffusion models, offer new opportunities for enhancing change detection accuracy. We propose a novel change detection framework that combines the strengths of Stable Diffusion models with the Structural Similarity Index (SSIM) to create robust and interpretable change maps.
  arXiv  Detail & Related papers  (2024-08-20T07:54:08Z)
• Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization [52.87635234206178]
  This paper proposes a new framework, namely MoNFAP, specifically tailored for multi-face manipulation detection and localization. The framework incorporates two novel modules: the Forgery-aware Unified Predictor (FUP) Module and the Mixture-of-Noises Module (MNM)
  arXiv  Detail & Related papers  (2024-08-05T08:35:59Z)
• ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
  We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
  arXiv  Detail & Related papers  (2024-03-21T17:52:08Z)
• Multimodal Transformer Using Cross-Channel attention for Object Detection in Remote Sensing Images [1.662438436885552]
  Multi-modal fusion has been determined to enhance the accuracy by fusing data from multiple modalities. We propose a novel multi-modal fusion strategy for mapping relationships between different channels at the early stage. By addressing fusion in the early stage, as opposed to mid or late-stage methods, our method achieves competitive and even superior performance compared to existing techniques.
  arXiv  Detail & Related papers  (2023-10-21T00:56:11Z)
• Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation [109.1912721224697]
  We present the Unified Frequency-Assisted transFormer framework, named UFAFormer, to address the DGM4 problem. By leveraging the discrete wavelet transform, we decompose images into several frequency sub-bands, capturing rich face forgery artifacts. Our proposed frequency encoder, incorporating intra-band and inter-band self-attentions, explicitly aggregates forgery features within and across diverse sub-bands.
  arXiv  Detail & Related papers  (2023-09-18T11:06:42Z)
• Remote Sensing Image Change Detection with Graph Interaction [1.8579693774597708]
  We propose a bitemporal image graph Interaction network for remote sensing change detection, namely BGINet-CD. Our model demonstrates superior performance compared to other state-of-the-art methods (SOTA) on the GZ CD dataset.
  arXiv  Detail & Related papers  (2023-07-05T03:32:49Z)
• DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing [94.24479528298252]
  DragGAN is an interactive point-based image editing framework that achieves impressive editing results with pixel-level precision. By harnessing large-scale pretrained diffusion models, we greatly enhance the applicability of interactive point-based editing on both real and diffusion-generated images. We present a challenging benchmark dataset called DragBench to evaluate the performance of interactive point-based image editing methods.
  arXiv  Detail & Related papers  (2023-06-26T06:04:09Z)
• Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection [7.324459578044212]
  Face presentation attack detection (PAD) is attracting a lot of attention and playing a key role in securing face recognition systems. We propose a dual-stream convolution neural networks (CNNs) framework to deal with unseen scenarios. We successfully prove the design of our proposed PAD solution in a step-wise ablation study.
  arXiv  Detail & Related papers  (2021-09-16T13:06:43Z)
• GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing [12.982905875008214]
  We propose an enhanced multi-scale network, dubbed GridDehazeNet+, for single image dehazing. It consists of three modules: pre-processing, backbone, and post-processing.
  arXiv  Detail & Related papers  (2021-03-25T17:35:36Z)

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.