# Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification

arXiv: https://arxiv.org/abs/2108.13098 · HTML: https://mlsanity.com/paper/2108.13098/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 55 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 30 of 30)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Meta-Learning with Latent Embedding Optimization](https://mlsanity.com/paper/1807.05960/) (2018)
- [Meta-Learning with Differentiable Convex Optimization](https://mlsanity.com/paper/1904.03758/) (2019)
- [TADAM: Task dependent adaptive metric for improved few-shot learning](https://mlsanity.com/paper/1805.10123/) (2018)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Bilinear CNNs for Fine-grained Visual Recognition](https://mlsanity.com/paper/1504.07889/) (2015)
- [Negative Margin Matters: Understanding Margin in Few-shot Classification](https://mlsanity.com/paper/2003.12060/) (2020)
- [Visual Attribute Transfer through Deep Image Analogy](https://mlsanity.com/paper/1705.01088/) (2017)
- [Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning](https://mlsanity.com/paper/1903.12290/) (2019)
- [Improved Few-Shot Visual Classification](https://mlsanity.com/paper/1912.03432/) (2019)
- [Part-based R-CNNs for Fine-grained Category Detection](https://mlsanity.com/paper/1407.3867/) (2014)
- [Compact Bilinear Pooling](https://mlsanity.com/paper/1511.06062/) (2015)
- [Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches](https://mlsanity.com/paper/2003.03836/) (2020)
- [Learning Attentive Pairwise Interaction for Fine-Grained Classification](https://mlsanity.com/paper/2002.10191/) (2020)
- [SFNet: Learning Object-aware Semantic Correspondence](https://mlsanity.com/paper/1904.01810/) (2019)
- [Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition](https://mlsanity.com/paper/1806.05372/) (2018)
- [Instance Credibility Inference for Few-Shot Learning](https://mlsanity.com/paper/2003.11853/) (2020)
- [Low-rank Bilinear Pooling for Fine-Grained Classification](https://mlsanity.com/paper/1611.05109/) (2016)
- [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)
- [TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification](https://mlsanity.com/paper/2003.06670/) (2020)
- [Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples](https://mlsanity.com/paper/1805.04288/) (2018)
- [Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition](https://mlsanity.com/paper/1911.07164/) (2019)
- [Learning to Compare Relation: Semantic Alignment for Few-Shot Learning](https://mlsanity.com/paper/2003.00210/) (2020)

## Built on by (most cited first, 8 of 8)

- [Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment](https://mlsanity.com/paper/2210.01439/) (2022)
- [Disentangled Feature Representation for Few-shot Image Classification](https://mlsanity.com/paper/2109.12548/) (2021)
- [Few-Shot Object Detection: Research Advances and Challenges](https://mlsanity.com/paper/2404.04799/) (2024)
- [Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/2207.00784/) (2022)
- [Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition](https://mlsanity.com/paper/2305.07180/) (2023)
- [A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning](https://mlsanity.com/paper/2210.12348/) (2022)
- [E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information Flow](https://mlsanity.com/paper/2207.06893/) (2022)
- [Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/2604.14958/) (2026)

## Abstract (arXiv)

The goal of few-shot fine-grained image classification is to recognize rarely seen fine-grained objects in the query set, given only a few samples of this class in the support set. Previous works focus on learning discriminative image features from a limited number of training samples for distinguishing various fine-grained classes, but ignore one important fact that spatial alignment of the discriminative semantic features between the query image with arbitrary changes and the support image, is also critical for computing the semantic similarity between each support-query pair. In this work, we propose an object-aware long-short-range spatial alignment approach, which is composed of a foreground object feature enhancement (FOE) module, a long-range semantic correspondence (LSC) module and a short-range spatial manipulation (SSM) module. The FOE is developed to weaken background disturbance and encourage higher foreground object response. To address the problem of long-range object feature misalignment between support-query image pairs, the LSC is proposed to learn the transferable long-range semantic correspondence by a designed feature similarity metric. Further, the SSM module is developed to refine the transformed support feature after the long-range step to align short-range misaligned features (or local details) with the query features. Extensive experiments have been conducted on four benchmark datasets, and the results show superior performance over most state-of-the-art methods under both 1-shot and 5-shot classification scenarios.