MAP-Seg: Semi-parametric Prototype Learning for Few-Shot Lodging Detection in Aerial Imagery
18th Asian Conference on Recent Challenges in Intelligent information and Database Systems, ACIIDS 2026, Kao-hsiung, Tayvan, 13 - 15 Nisan 2026, cilt.2964 CCIS, ss.560-572, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 2964 CCIS
- Doi Numarası: 10.1007/978-981-92-0068-9_38
- Basıldığı Şehir: Kao-hsiung
- Basıldığı Ülke: Tayvan
- Sayfa Sayıları: ss.560-572
- Anahtar Kelimeler: Agricultural Vision, Few-Shot Learning, Prototype Learning
- Kocaeli Üniversitesi Adresli: Evet
Özet
Few-shot semantic segmentation is a promising approach for agricultural monitoring scenarios where dense annotations are expensive, yet existing methods often rely on opaque encoder–decoder models or manual clustering pipelines, limiting reliability in real-world systems. We propose MAP-Seg, a prototype-based few-shot segmentation framework for rice lodging detection from aerial imagery, built upon a frozen self-supervised vision foundation model (DINO). MAP-Seg reformulates clustering as a differentiable, end-to-end learnable process through learnable prototypes and a cluster selector, eliminating the non-differentiability and manual intervention of traditional K-means–based workflows. Lightweight feature adaptation and multi-scale representations are further incorporated to address domain shift while maintaining parameter efficiency. By producing segmentation through a structured set of prototypes, MAP-Seg offers a more interpretable and controllable intermediate representation, which is desirable for trustworthy deployment in agricultural cyber-physical systems. Experiments on real-world aerial datasets demonstrate consistent improvements over conventional encoder–decoder methods under strict few-shot settings.