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Historical street-view imagery records urban evolution, but uneven coverage leaves substantial gaps in historical records. Generating plausible past appearances requires restoring changed structures w...
arXiv:2603. 03143v2 Announce Type: replace-cross Abstract: Leveraging the priors of 2D diffusion models for 3D editing has emerged as a promising paradigm.
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.
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.
Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing demands with the practicality required by everyday users. However, editing models trained primarily for single-turn edits often break down in multi-turn editing--the natural interactive setting where a user iteratively refines an image based on the model's own previous outputs.
arXiv:2610.01870v1 Announce Type: new Abstract: Urban environments are shaped by design choices with long-term implications for health, safety, and quality of life, yet evaluating proposed interventi...