CoRegOVCD: Consistency-Regularized Open-Vocabulary Change Detection

• URL: http://arxiv.org/abs/2604.02160v1
• Date: Thu, 02 Apr 2026 15:28:29 GMT
• Title: CoRegOVCD: Consistency-Regularized Open-Vocabulary Change Detection
• Abstract summary: Open-vocabulary change detection (OVCD) asks for the change mask of a queried concept.<n>We propose Consistency-Regularized Open-Vocabulary Change$RegOVCD, a training-free framework that reformulates concept-specific change as posterior discrepancy.<n>CoRegOVCD consistently improves over the strongest previous training-free baseline by 2.24 to 4.98 F1$_C points.
• Score: 16.119542774408597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote sensing change detection (CD) aims to identify where land-cover semantics change across time, but most existing methods still assume a fixed label space and therefore cannot answer arbitrary user-defined queries. Open-vocabulary change detection (OVCD) instead asks for the change mask of a queried concept. In the fully training-free setting, however, dense concept responses are difficult to compare directly across dates: appearance variation, weak cross-concept competition, and the spatial continuity of many land-cover categories often produce noisy, fragmented, and semantically unreliable change evidence. We propose Consistency-Regularized Open-Vocabulary Change Detection (CoRegOVCD), a training-free dense inference framework that reformulates concept-specific change as calibrated posterior discrepancy. Competitive Posterior Calibration (CPC) and the Semantic Posterior Delta (SPD) convert raw concept responses into competition-aware queried-concept posteriors and quantify their cross-temporal discrepancy, making semantic change evidence more comparable without explicit instance matching. Geometry-Token Consistency Gate (GeoGate) and Regional Consensus Discrepancy (RCD) further suppress unsupported responses and improve spatial coherence through geometry-aware structural verification and regional consensus. Across four benchmarks spanning building-oriented and multi-class settings, CoRegOVCD consistently improves over the strongest previous training-free baseline by 2.24 to 4.98 F1$_C$ points and reaches a six-class average of 47.50% F1$_C$ on SECOND.

Related papers

• CogVis: Must Open-Vocabulary Change Detection Perceive the Scene Anew for Every Query? [12.117404924867804]
  Earth-surface monitoring requires change detection models capable of recognizing arbitrary semantic categories.<n>Open-Vocabulary Change Detection (OVCD) addresses this need.<n>We propose CogVis, a cognitive memory-guided framework that reformulates OVCD as a perception-memory-verification paradigm.
  arXiv  Detail & Related papers  (2026-08-06T15:18:35Z)
• 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)
• Spatially Selective Self-Training for Unsupervised Building Change Detection [8.884818023657276]
  Unsupervised building change detection aims to learn building-change masks from unlabeled bi-temporal remote sensing images.<n>SST-CD is a spatially selective self-training framework that reformulates fully label-free building change detection as end-to-end detector learning.<n>Experiments show that SST-CD achieves F1 scores of 83.08%, 91.69%, and 86.60%, respectively, outperforming existing unsupervised and label-free baselines.
  arXiv  Detail & Related papers  (2026-06-09T12:31:16Z)
• 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)
• MemOVCD: Training-Free Open-Vocabulary Change Detection via Cross-Temporal Memory Reasoning and Global-Local Adaptive Rectification [25.164480665248792]
  Open-vocabulary change detection aims to identify semantic changes in bi-temporal remote sensing images without predefined categories.<n>We propose MemOVCD, a training-free open-vocabulary change detection framework based on cross-temporal memory reasoning and global-local adaptive rectification.
  arXiv  Detail & Related papers  (2026-04-29T15:05:37Z)
• Back to Source: Open-Set Continual Test-Time Adaptation via Domain Compensation [18.70132691409063]
  Test-Time Adaptation aims to mitigate distributional shifts between training and test domains during inference time.<n>The coupling of domain and semantic shifts often collapses the feature space, severely degrading both classification and out-of-distribution detection.<n>We propose DOmain COmpensation (DOCO), a lightweight and effective framework that robustly performs domain adaptation and OOD detection in a synergistic, closed loop.
  arXiv  Detail & Related papers  (2026-04-23T15:29:29Z)
• Towards Domain-Generalized Open-Vocabulary Object Detection: A Progressive Domain-invariant Cross-modal Alignment Method [59.30562121800656]
  Open-Vocabulary Object Detection has achieved remarkable success in generalizing to novel categories.<n>We provide a principled revisit of the OVOD paradigm, uncovering a fundamental vulnerability.<n>We propose Progressive Domain-invariant Cross-modal Alignment (PICA)
  arXiv  Detail & Related papers  (2026-03-29T07:39:31Z)
• SGMatch: Semantic-Guided Non-Rigid Shape Matching with Flow Regularization [43.12848612518533]
  We introduce SGMatch, a learning-based framework for semantic-guided non-rigid shape matching.<n> Specifically, we design a Semantic-Guided Local Cross-Attention module that integrates semantic features from vision foundation models into geometric descriptors.<n>We introduce a regularization objective based on conditional flow matching, which supervises a time-varying velocity field to encourage spatial smoothness of the recovered correspondences.
  arXiv  Detail & Related papers  (2026-03-13T12:31:43Z)
• TaCo: Capturing Spatio-Temporal Semantic Consistency in Remote Sensing Change Detection [54.22717266034045]
  Ta-Co is a consistent semantic network for temporal semantic transitions.<n>We show that Ta-Co consistently achieves SOTA performance on remote sensing detection tasks.<n>This design can yield substantial gains without any additional computational overhead during inference.
  arXiv  Detail & Related papers  (2025-11-25T13:44:29Z)
• Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection [51.56484100374058]
  We introduce a modular pipeline that improves spatial and temporal robustness without altering existing change detection networks.<n>A diffusion module synthesizes intermediate morphing frames that bridge large appearance gaps, enabling RoMa to estimate stepwise correspondences.<n>Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show consistent gains in both registration accuracy and downstream change detection.
  arXiv  Detail & Related papers  (2025-11-11T08:40:28Z)
• DiffRegCD: Integrated Registration and Change Detection with Diffusion Features [74.3102451211493]
  We present DiffRegCD, an integrated framework that unifies dense registration and change detection in a single model.<n>Experiments on aerial (LEVIR-CD, DSIFN-CD, WHU-CD, SYSU-CD) and ground level (VL-CMU-CD) datasets show that DiffRegCD consistently surpasses recent baselines.
  arXiv  Detail & Related papers  (2025-11-11T07:32:19Z)
• Leveraging Geometric Priors for Unaligned Scene Change Detection [53.523333385654546]
  Unaligned Scene Change Detection aims to detect scene changes between image pairs captured at different times without assuming viewpoint alignment.<n>We introduce geometric priors for the first time to address the core challenges of unaligned SCD.<n>We propose a training-free framework that integrates them with the powerful representations of a visual foundation model.
  arXiv  Detail & Related papers  (2025-09-14T14:31:08Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images [22.72105435238235]
  We propose a Semantic Change (SCanFormer) to explicitly model the 'from-to' semantic transitions between the bi-temporal RSIss. Then, we introduce a semantic learning scheme to leverage the Transformer-temporal constraints, which are coherent to the SCD task, to guide the learning of semantic changes. The resulting network (SCanNet) outperforms the baseline method in terms of both detection of critical semantic changes and semantic consistency in the obtained bi-temporal results.
  arXiv  Detail & Related papers  (2022-12-10T08:49:19Z)

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.