Local Foreground Selection Aware Attentive Feature Reconstruction for Few-Shot Fine-Grained Plant Species Classification

Aisha Zulfiqar, Ebroul Izquierdo

2025

Abstract

Plant species exhibit subtle distinctions, requiring a reduction in intra-class variation and an increase in inter-class differences to improve accuracy. This paper addresses plant species classification using a limited number of labelled samples and introduces a novel Local Foreground Selection(LFS) attention mechanism. Based on the proposed attention Local Foreground Selection Module(LFSM) is a straightforward module designed to generate discriminative support and query feature maps. It operates by integrating two types of attention: local attention, which captures local spatial details to enhance feature discrimination and increase inter-class differentiation, and foreground selection attention, which emphasizes the foreground plant object while mitigating background interference. By focusing on the foreground, the query and support features selectively highlight relevant feature sequences and disregard less significant background sequences, thereby reducing intra-class differences. Experimental results from three plant species datasets demonstrate the effectiveness of the proposed LFS attention and its complementary advantages over previous feature reconstruction methods.

Paper Citation

in Harvard Style

Zulfiqar A. and Izquierdo E. (2025). Local Foreground Selection Aware Attentive Feature Reconstruction for Few-Shot Fine-Grained Plant Species Classification. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 532-539. DOI: 10.5220/0013184000003912

in Bibtex Style

@conference{visapp25,
author={Aisha Zulfiqar and Ebroul Izquierdo},
title={Local Foreground Selection Aware Attentive Feature Reconstruction for Few-Shot Fine-Grained Plant Species Classification},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={532-539},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013184000003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Local Foreground Selection Aware Attentive Feature Reconstruction for Few-Shot Fine-Grained Plant Species Classification
SN - 978-989-758-728-3
AU - Zulfiqar A.
AU - Izquierdo E.
PY - 2025
SP - 532
EP - 539
DO - 10.5220/0013184000003912
PB - SciTePress