ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing

• URL: http://arxiv.org/abs/2605.15375v1
• Date: Thu, 14 May 2026 20:04:16 GMT
• Title: ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing
• Abstract summary: ChangeFlow is a generative framework that reformulates change detection as the synthesis of a change mask in latent space via rectified flow.<n>It achieves an average F1 of 80.4%, improving by 1.3 points on average over the previous best method.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Remote sensing change detection (RSCD) aims to localise changes between two images of the same geographic region. In practice, change masks often follow region-level annotation conventions rather than purely local appearance differences, making them context-dependent and occasionally ambiguous. Most state-of-the-art methods utilise per-pixel discriminative classification, which produces a single prediction per input and fails to explicitly model the changed region as a coherent whole. A natural alternative is generative formulation, which can model a distribution of plausible masks, enabling sampling to capture ambiguity and encourage global consistency. However, existing generative RSCD approaches typically lag behind strong discriminative baselines due to the high computational cost of pixel-space generation and the complexity of their conditioning mechanisms. To address the limitations of prior discriminative and generative methods, we propose ChangeFlow, a generative framework that reformulates change detection as the synthesis of a change mask in latent space via rectified flow. ChangeFlow is guided by a structured yet lightweight conditioning signal, and its stochastic design naturally supports sampling-based prediction ensembling. Namely, aggregating multiple predicted change masks improves robustness, while sample agreement provides a practical confidence estimation that highlights ambiguous regions. Across four benchmarks, ChangeFlow achieves an average F1 of 80.4\%, improving by 1.3 points on average over the previous best method, while maintaining inference speed comparable to recent strong baselines. Project page: https://blaz-r.github.io/changeflow_cd

Related papers

• ModaFlow: Modality-Aware Flow Matching for High-Fidelity Virtual Try-On [48.40260700745353]
  We present ModaFlow, a modality-aware flow-matching based framework for high-fidelity virtual try-on.<n>We show that ModaFlow achieves state-of-the-art results in both qualitative and quantitative evaluations.
  arXiv  Detail & Related papers  (2026-06-26T06:59:50Z)
• ReA-OVCD: Reliability-Aware Open-Vocabulary Change Detection via Semantic and Spatial Refinement [8.855406881644702]
  We propose an efficient training-free Reliability-Aware Open-Vocabulary Change Detection framework.<n>It first derives candidate change regions from pixel-wise semantic discrepancies to ensure flexible and detailed localization.<n>It then introduces a collaborative refinement strategy to explicitly model change validity from both semantic and spatial perspectives.
  arXiv  Detail & Related papers  (2026-06-18T10:05:45Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• Towards Generalized Image Manipulation Localization via Score-based Model [38.03898206912958]
  We propose DiffIML, a novel framework that introduces score-based generative modeling to IML.<n>Under this formulation, diffusion models serve as an effective numerical solver for the learned score function.<n>Experiments on eight non-generative and three generative benchmarks demonstrate that DiffIML consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-16T08:39:54Z)
• ORSIFlow: Saliency-Guided Rectified Flow for Optical Remote Sensing Salient Object Detection [16.437554336020757]
  Optical Remote Sensing Image Salient Object Detection (ORSI-SOD) remains challenging due to complex backgrounds, low contrast, irregular object shapes, and large variations in object scale.<n>We propose ORSIFlow, a saliency-guided rectified flow framework that reformulates ORSI-SOD as a deterministic latent flow generation problem.<n>To enhance saliency awareness, we design a Salient Feature Discriminator for global semantic discrimination and a Salient Feature Calibrator for precise boundary refinement.
  arXiv  Detail & Related papers  (2026-03-30T15:33:26Z)
• Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations [0.0]
  Unsupervised change detection (UCD) in remote sensing aims to localise semantic changes between two images of the same region without relying on labelled data during training.<n>We propose MaSoN, an end-to-end UCD framework that synthesises diverse changes directly in the latent feature space during training.<n>It generates changes that are dynamically estimated using feature statistics of target data, enabling diverse yet data-driven variation aligned with the target domain.
  arXiv  Detail & Related papers  (2026-02-23T14:27:36Z)
• Referring Change Detection in Remote Sensing Imagery [49.841833753558575]
  We introduce Referring Change Detection (RCD), which leverages natural language prompts to detect specific classes of changes in remote sensing images.<n>We propose a two-stage framework consisting of (I) textbfRCDNet, a cross-modal fusion network designed for referring change detection, and (II) textbfRCDGen, a diffusion-based synthetic data generation pipeline.
  arXiv  Detail & Related papers  (2025-12-12T16:57:12Z)
• Simulating Distribution Dynamics: Liquid Temporal Feature Evolution for Single-Domain Generalized Object Detection [58.25418970608328]
  Single-Domain Generalized Object Detection (Single-DGOD) aims to transfer a detector trained on one source domain to multiple unknown domains.<n>Existing methods for Single-DGOD typically rely on discrete data augmentation or static perturbation methods to expand data diversity.<n>We propose a new method, which simulates the progressive evolution of features from the source domain to simulated latent distributions.
  arXiv  Detail & Related papers  (2025-11-13T03:10:39Z)
• OSCAR: Orthogonal Stochastic Control for Alignment-Respecting Diversity in Flow Matching [14.664226708184676]
  Flow-based text-to-image models follow deterministic trajectories, forcing users to repeatedly sample to discover diverse modes.<n>We present a training-free, inference-time control mechanism that makes the flow itself diversity-aware.
  arXiv  Detail & Related papers  (2025-10-10T07:07:19Z)
• GeneralizeFormer: Layer-Adaptive Model Generation across Test-Time Distribution Shifts [58.95913531746308]
  We consider the problem of test-time domain generalization, where a model is trained on several source domains and adjusted on target domains never seen during training.<n>We propose to generate multiple layer parameters on the fly during inference by a lightweight meta-learned transformer, which we call textitGeneralizeFormer.
  arXiv  Detail & Related papers  (2025-02-15T10:10:49Z)
• PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
  PseudoNeg-MAE enhances global feature representation of point cloud masked autoencoders by making them both discriminative and sensitive to transformations.<n>We propose a novel loss that explicitly penalizes invariant collapse, enabling the network to capture richer transformation cues while preserving discriminative representations.
  arXiv  Detail & Related papers  (2024-09-24T07:57:21Z)
• Semantic Latent Decomposition with Normalizing Flows for Face Editing [43.41509627246366]
  Navigating in the latent space of StyleGAN has shown effectiveness for face editing. The resulting methods usually encounter challenges in complicated navigation due to the entanglement among different attributes in the latent space. This paper proposes a novel framework, termed SDFlow, with a semantic decomposition in original latent space.
  arXiv  Detail & Related papers  (2023-09-11T08:59:15Z)
• GFlowOut: Dropout with Generative Flow Networks [76.59535235717631]
  Monte Carlo Dropout has been widely used as a relatively cheap way for approximate Inference. Recent works show that the dropout mask can be viewed as a latent variable, which can be inferred with variational inference. GFlowOutleverages the recently proposed probabilistic framework of Generative Flow Networks (GFlowNets) to learn the posterior distribution over dropout masks.
  arXiv  Detail & Related papers  (2022-10-24T03:00:01Z)
• Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning [23.203687716051697]
  A robust unsupervised approach is proposed for small area change detection from synthetic aperture radar (SAR) images. A multi-scale superpixel reconstruction method is developed to generate a difference image (DI) A two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes.
  arXiv  Detail & Related papers  (2020-11-22T12:50:08Z)

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.