# Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation

arXiv: https://arxiv.org/abs/2207.06989 · HTML: https://mlsanity.com/paper/2207.06989/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 51 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 30 of 34)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [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)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [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 for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?](https://mlsanity.com/paper/2003.11539/) (2020)
- [Meta-learning with differentiable closed-form solvers](https://mlsanity.com/paper/1805.08136/) (2018)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions](https://mlsanity.com/paper/1812.03664/) (2018)
- [Cross Attention Network for Few-shot Classification](https://mlsanity.com/paper/1910.07677/) (2019)
- [Boosting Few-Shot Visual Learning with Self-Supervision](https://mlsanity.com/paper/1906.05186/) (2019)
- [Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes](https://mlsanity.com/paper/1906.07697/) (2019)
- [Few-Shot Image Recognition by Predicting Parameters from Activations](https://mlsanity.com/paper/1706.03466/) (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)
- [Finding Task-Relevant Features for Few-Shot Learning by Category Traversal](https://mlsanity.com/paper/1905.11116/) (2019)
- [Self-Supervised Generalisation with Meta Auxiliary Learning](https://mlsanity.com/paper/1901.08933/) (2019)
- [When Does Self-supervision Improve Few-shot Learning?](https://mlsanity.com/paper/1910.03560/) (2019)
- [Relational Embedding for Few-Shot Classification](https://mlsanity.com/paper/2108.09666/) (2021)
- [Hierarchically Structured Meta-learning](https://mlsanity.com/paper/1905.05301/) (2019)
- [A Universal Representation Transformer Layer for Few-Shot Image Classification](https://mlsanity.com/paper/2006.11702/) (2020)
- [Few-Shot Learning with Embedded Class Models and Shot-Free Meta Training](https://mlsanity.com/paper/1905.04398/) (2019)

## Built on by (most cited first, 14 of 14)

- [Prompt-based Distribution Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2312.09553/) (2023)
- [Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization](https://mlsanity.com/paper/2403.14198/) (2024)
- [Graph based Environment Representation for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2301.04352/) (2023)
- [Generalized Universal Domain Adaptation with Generative Flow Networks](https://mlsanity.com/paper/2305.04466/) (2023)
- [PiTe: Pixel-Temporal Alignment for Large Video-Language Model](https://mlsanity.com/paper/2409.07239/) (2024)
- [Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation](https://mlsanity.com/paper/2403.14232/) (2024)
- [RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning](https://mlsanity.com/paper/2303.06679/) (2023)
- [AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation](https://mlsanity.com/paper/2403.07030/) (2024)
- [MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation](https://mlsanity.com/paper/2404.19644/) (2024)
- [Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization](https://mlsanity.com/paper/2305.15889/) (2023)
- [Disentangled Representation Learning with Transmitted Information Bottleneck](https://mlsanity.com/paper/2311.01686/) (2023)
- [S-JEA: Stacked Joint Embedding Architectures for Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/2305.11701/) (2023)
- [KSG: Knowledge and Skill Graph](https://mlsanity.com/paper/2209.05698/) (2022)
- [Learning to Reweight for Graph Neural Network](https://mlsanity.com/paper/2312.12475/) (2023)

## Abstract (arXiv)

In this paper, we mainly focus on the problem of how to learn additional feature representations for few-shot image classification through pretext tasks (e.g., rotation or color permutation and so on). This additional knowledge generated by pretext tasks can further improve the performance of few-shot learning (FSL) as it differs from human-annotated supervision (i.e., class labels of FSL tasks). To solve this problem, we present a plug-in Hierarchical Tree Structure-aware (HTS) method, which not only learns the relationship of FSL and pretext tasks, but more importantly, can adaptively select and aggregate feature representations generated by pretext tasks to maximize the performance of FSL tasks. A hierarchical tree constructing component and a gated selection aggregating component is introduced to construct the tree structure and find richer transferable knowledge that can rapidly adapt to novel classes with a few labeled images. Extensive experiments show that our HTS can significantly enhance multiple few-shot methods to achieve new state-of-the-art performance on four benchmark datasets. The code is available at: https://github.com/remiMZ/HTS-ECCV22.