# Local Foreground Selection aware Attentive Feature Reconstruction for few-shot fine-grained plant species classification

arXiv: https://arxiv.org/abs/2501.06909 · HTML: https://mlsanity.com/paper/2501.06909/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 12 Jan 2025.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions](https://mlsanity.com/paper/1812.03664/) (2018)
- [CrossTransformers: spatially-aware few-shot transfer](https://mlsanity.com/paper/2007.11498/) (2020)
- [Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning](https://mlsanity.com/paper/1903.12290/) (2019)
- [Few-Shot Classification with Feature Map Reconstruction Networks](https://mlsanity.com/paper/2012.01506/) (2020)
- [Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/1908.01313/) (2019)
- [Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition](https://mlsanity.com/paper/2004.00705/) (2020)
- [BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification](https://mlsanity.com/paper/2011.14311/) (2020)
- [Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment](https://mlsanity.com/paper/2210.01439/) (2022)
- [TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples](https://mlsanity.com/paper/2005.13820/) (2020)
- [NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification](https://mlsanity.com/paper/2106.06988/) (2021)
- [Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification](https://mlsanity.com/paper/2211.17161/) (2022)
- [Few-shot Learning for Domain-specific Fine-grained Image Classification](https://mlsanity.com/paper/1907.09647/) (2019)
- [Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition](https://mlsanity.com/paper/1911.07164/) (2019)
- [Enhancing Few-shot Image Classification with Cosine Transformer](https://mlsanity.com/paper/2211.06828/) (2022)

## Abstract (arXiv)

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.