دورية أكاديمية

Practical Techniques for Vision-Language Segmentation Model in Remote Sensing

التفاصيل البيبلوغرافية
العنوان: Practical Techniques for Vision-Language Segmentation Model in Remote Sensing
المؤلفون: Y. Lin, K. Suzuki, S. Sogo
المصدر: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-2-2024, Pp 203-210 (2024)
بيانات النشر: Copernicus Publications, 2024.
سنة النشر: 2024
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Applied optics. Photonics
مصطلحات موضوعية: Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
الوصف: Traditional semantic segmentation models often struggle with poor generalizability in zero-shot scenarios such as recognizing attributes unseen in the training labels. On the other hands, language-vision models (VLMs) have shown promise in improving performance on zero-shot tasks by leveraging semantic information from textual inputs and fusing this information with visual features. However, existing VLM-based methods do not perform as effectively on remote sensing data due to the lack of such data in their training datasets. In this paper, we introduce a two-stage fine-tuning approach for a VLM-based segmentation model using a large remote sensing image-caption dataset, which we created using an existing image-caption model. Additionally, we propose a modified decoder and a visual prompt technique using a saliency map to enhance segmentation results. Through these methods, we achieve superior segmentation performance on remote sensing data, demonstrating the effectiveness of our approach.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1682-1750
2194-9034
Relation: https://isprs-archives.copernicus.org/articles/XLVIII-2-2024/203/2024/isprs-archives-XLVIII-2-2024-203-2024.pdf; https://doaj.org/toc/1682-1750; https://doaj.org/toc/2194-9034
DOI: 10.5194/isprs-archives-XLVIII-2-2024-203-2024
URL الوصول: https://doaj.org/article/7e8446aedbdc4aa5b2803db9eb0912bc
رقم الأكسشن: edsdoj.7e8446aedbdc4aa5b2803db9eb0912bc
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:16821750
21949034
DOI:10.5194/isprs-archives-XLVIII-2-2024-203-2024