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Diverse Instance Generation via Diffusion Models for Enhanced Few-Shot Object Detection in Remote Sensing Images — arXiv:2511.18031

Source: arXiv (en)
Original: https://arxiv.org/abs/2511.18031
License (as archived): CC0-1.0
Archived: 2026-10-16T11:00:10.874Z
SHA-256 of the archived text: 685cc4ac4bbf7f41f72efea33c63f629e14cbf6568451cf80b95b673347d549b
Stable URL of this page: https://metraipolis.com/documento/dc_6b352fbc87f6

How to cite

Diverse Instance Generation via Diffusion Models for Enhanced Few-Shot Object Detection in Remote Sensing Images — arXiv:2511.18031 — arXiv (en), https://arxiv.org/abs/2511.18031 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_6b352fbc87f6 (Metraipolis archive dc_6b352fbc87f6, sha256 685cc4ac4bbf7f41f72efea33c63f629e14cbf6568451cf80b95b673347d549b)

BibTeX

@misc{dc6b352fbc87f6,
  title = {Diverse Instance Generation via Diffusion Models for Enhanced Few-Shot Object Detection in Remote Sensing Images — arXiv:2511.18031},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2511.18031},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_6b352fbc87f6; sha256 685cc4ac4bbf7f41f72efea33c63f629e14cbf6568451cf80b95b673347d549b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2511.18031. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that is particularly prominent in numerous remote sensing applications such as endangered species monitoring and disaster assessment. Existing FSOD methods for remote sensing images (RSIs) have achieved promising progress but remain constrained by the limited diversity of instances. To address this issue, we propose a novel framework that can leverage a diffusion model pretrained on large-scale natural images to synthesize diverse remote sensing instances, thereby improving the performance of few-shot object detectors. Instead of directly synthesizing complete remote sensing images, we first generate instance-level slices via a specialized slice-to-slice module, and then embed these slices into full-scale imagery for enhanced data augmentation. To further adapt diffusion models for remote sensing scenarios, we develop a class-agnostic image inversion module that can invert remote sensing instance slices into semantic space. Additionally, we introduce contrastive loss to semantically align the synthesized images with their corresponding classes. Experimental results show that our method hasachieved an average performance improvement of 4.4% across multiple datasets and various approaches. Ablation experiments indicate that the elaborately designed inversion module can effectively enhance the performance of FSOD methods, and the semantic contrastive loss can further boost the performance.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-10 in cs.CV).
Categorie arXiv: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2511.18031 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T10:02:34.419Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-10?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).