arXiv Computer Vision
DS-BiCA-Net: Pair-Preserving Cross-Temporal Modeling for Remote Sensing Change Captioning
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arXiv Computer Vision
Sep 24
From Change Captions to Change Detection: Semantic-Appearance Agreement Framework for Remote Sensing Change Detection
The paper proposes a weakly supervised remote sensing change detection method that uses change captions as the sole supervision signal, eliminating the need for pixel‑level change masks. It introduces a caption‑driven generation pipeline to create bi‑temporal image pairs with controlled changes and a Semantic‑Appearance Agreement Framework (SAAF) that fuses caption‑grounded semantic responses with RGB differences for accurate change localization. Experiments on the Flair‑RSGen and WHU‑CDC datasets demonstrate that SAAF outperforms existing limited‑supervision baselines in macro‑averaged IoU and F1 metrics.
arXiv AI
Jul 28
ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing
arXiv:2605. 15375v2 Announce Type: replace-cross Abstract: Remote sensing change detection (RSCD) localises changes between two images of the same geographic region.
arXiv AI
Jul 1
JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering
arXiv:2606. 31745v1 Announce Type: cross Abstract: Remote sensing change detection (CD) traditionally focuses on pixel-level binary segmentation, which identifies where changes occur but neither what nor why.
arXiv Machine Learning
Sep 23
MGRL-RSCC: Multi-Granularity Reward Reinforcement Learning for Fine-Grained Remote Sensing Change Captioning
The paper introduces MGRL-RSCC, a multi‑granularity reward reinforcement learning framework for Remote Sensing Change Captioning (RSCC). It uses a CNN with hierarchical self‑attention to extract visual features, a Transformer decoder for visual‑to‑linguistic translation, and a dual‑decoding strategy combined with token‑level supervised learning and self‑critical reinforcement learning. Three reward functions—linguistic fluency, change state consistency, and structural‑semantic relevance—are employed to improve caption quality and reduce exposure bias and conservative generation.
arXiv AI
Jun 30
CCRC: A Change-Aware Captioning and Reasoning Chain for Image Change Captioning and Segmentation
arXiv:2606. 28724v1 Announce Type: cross Abstract: Understanding and localizing subtle changes between paired images is critical for tasks such as surveillance and image editing.
arXiv Computer Vision
1d ago
ChangeJEPA: Predict Before You Compare for Remote Sensing Change Detection
arXiv:2611.05570v1 Announce Type: new Abstract: Remote sensing change detection is typically formulated as Siamese feature comparison, where two images are encoded independently and a decoder determi...