通过关注植物主体，减少类内差异，提升细粒度植物分类准确率。
Local Foreground Selection aware Attentive Feature Reconstruction for few-shot fine-grained plant species classification
- 设计局部前景选择注意力机制，聚焦植物主体特征
- 在三个数据集上实现更高分类精度，优于已有方法
- 适合小样本细粒度植物识别任务，尤其关注背景干扰问题
植物物种具有显著的类内差异和微弱的类间差异。为提高分类准确性，需减小类内差异并增大类间差异。本文针对少量标注样本下的植物物种分类问题，提出一种新型局部前景选择（LFS）注意力机制。LFS是一种简洁模块，用于生成具有判别性的支持与查询特征图。其结合两种注意力：局部注意力捕捉局部空间细节以增强特征区分度、促进类间差异；前景选择注意力强调植物主体，抑制背景干扰。通过聚焦前景，查询与支持特征能选择性突出相关特征序列，忽略不重要的背景序列，从而降低类内差异。在三个植物物种数据集上的实验表明，所提LFS注意力机制有效，且相较于以往特征重建方法具互补优势。
原文摘要 · Abstract (English)
Plant species exhibit significant intra-class variation and minimal inter-class variation. To enhance classification accuracy, it is essential to reduce intra-class variation while maximizing inter-class variation. This paper addresses plant species classification using a limited number of labelled samples and introduces a novel Local Foreground Selection(LFS) attention mechanism. LFS 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 mechanism and its complementary advantages over previous feature reconstruction methods.
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