arXiv:2608. 03822v1 Announce Type: cross Abstract: Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation.
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Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation. Although generative models provide a promising means of data augmentation, existing methods often yield implausible spatial layouts of flooded regions and distort scene structures.
arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.
arXiv:2606. 26204v1 Announce Type: new Abstract: Floods frequently impact regions around the world.
FluidRain is a lightweight video deraining model that leverages a divergence‑free rain flow field to guide Loop‑in‑Loop attention across scales and neighboring frames, eliminating the need for explicit motion alignment. By projecting estimated rain‑flow onto a divergence‑free subspace, the method steers window attention along rain streaks, enabling efficient temporal aggregation with only 0.80 M parameters. Experiments on four benchmarks demonstrate competitive performance against larger models, and the authors introduce a new RainSyn‑Gust dataset and a physics‑based no‑reference metric for evaluating real‑rain removal.
arXiv:2608. 10544v1 Announce Type: cross Abstract: Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations.
arXiv:2505. 04486v4 Announce Type: replace-cross Abstract: Flow matching models have shown great potential in image generation tasks among probabilistic generative models.