# Multi-scale Adaptive Task Attention Network for Few-Shot Learning

arXiv: https://arxiv.org/abs/2011.14479 · HTML: https://mlsanity.com/paper/2011.14479/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Sparse Spatial Transformers for Few-Shot Learning](https://mlsanity.com/paper/2109.12932/) (2021)
- [Multi-level Metric Learning for Few-shot Image Recognition](https://mlsanity.com/paper/2103.11383/) (2021)
- [Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation](https://mlsanity.com/paper/2207.08547/) (2022)
- [Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning](https://mlsanity.com/paper/2110.09374/) (2021)
- [A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning](https://mlsanity.com/paper/2210.12348/) (2022)
- [Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification](https://mlsanity.com/paper/2103.11384/) (2021)
- [Finding Significant Features for Few-Shot Learning using Dimensionality Reduction](https://mlsanity.com/paper/2107.06992/) (2021)
- [SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification](https://mlsanity.com/paper/2305.09062/) (2023)
- [Learning Primitive-aware Discriminative Representations for Few-shot Learning](https://mlsanity.com/paper/2208.09717/) (2022)

## Abstract (arXiv)

The goal of few-shot learning is to classify unseen categories with few labeled samples. Recently, the low-level information metric-learning based methods have achieved satisfying performance, since local representations (LRs) are more consistent between seen and unseen classes. However, most of these methods deal with each category in the support set independently, which is not sufficient to measure the relation between features, especially in a certain task. Moreover, the low-level information-based metric learning method suffers when dominant objects of different scales exist in a complex background. To address these issues, this paper proposes a novel Multi-scale Adaptive Task Attention Network (MATANet) for few-shot learning. Specifically, we first use a multi-scale feature generator to generate multiple features at different scales. Then, an adaptive task attention module is proposed to select the most important LRs among the entire task. Afterwards, a similarity-to-class module and a fusion layer are utilized to calculate a joint multi-scale similarity between the query image and the support set. Extensive experiments on popular benchmarks clearly show the effectiveness of the proposed MATANet compared with state-of-the-art methods.