# Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition

arXiv: https://arxiv.org/abs/2009.04724 · HTML: https://mlsanity.com/paper/2009.04724/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 15 of 15)

- [What does a platypus look like? Generating customized prompts for zero-shot image classification](https://mlsanity.com/paper/2209.03320/) (2022)
- [Open-Set Image Tagging with Multi-Grained Text Supervision](https://mlsanity.com/paper/2310.15200/) (2023)
- [Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation](https://mlsanity.com/paper/2207.06989/) (2022)
- [Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?](https://mlsanity.com/paper/2306.01220/) (2023)
- [Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images](https://mlsanity.com/paper/2311.01064/) (2023)
- [Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning](https://mlsanity.com/paper/2109.01295/) (2021)
- [Reference-Limited Compositional Zero-Shot Learning](https://mlsanity.com/paper/2208.10046/) (2022)
- [Shaping Visual Representations with Attributes for Few-Shot Recognition](https://mlsanity.com/paper/2112.06398/) (2021)
- [Compositional Prototypical Networks for Few-Shot Classification](https://mlsanity.com/paper/2306.06584/) (2023)
- [Dual-View Data Hallucination with Semantic Relation Guidance for Few-Shot Image Recognition](https://mlsanity.com/paper/2401.07061/) (2024)
- [Harnessing the Power of Local Representations for Few-Shot Classification](https://mlsanity.com/paper/2407.01967/) (2024)
- [Interpretable Few-shot Learning with Online Attribute Selection](https://mlsanity.com/paper/2211.09107/) (2022)
- [Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2408.17083/) (2024)
- [An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers](https://mlsanity.com/paper/2412.06149/) (2024)
- [CausalFSFG: Rethinking Few-Shot Fine-Grained Visual Categorization from Causal Perspective](https://mlsanity.com/paper/2512.21617/) (2025)

## Abstract (arXiv)

The purpose of few-shot recognition is to recognize novel categories with a limited number of labeled examples in each class. To encourage learning from a supplementary view, recent approaches have introduced auxiliary semantic modalities into effective metric-learning frameworks that aim to learn a feature similarity between training samples (support set) and test samples (query set). However, these approaches only augment the representations of samples with available semantics while ignoring the query set, which loses the potential for the improvement and may lead to a shift between the modalities combination and the pure-visual representation. In this paper, we devise an attributes-guided attention module (AGAM) to utilize human-annotated attributes and learn more discriminative features. This plug-and-play module enables visual contents and corresponding attributes to collectively focus on important channels and regions for the support set. And the feature selection is also achieved for query set with only visual information while the attributes are not available. Therefore, representations from both sets are improved in a fine-grained manner. Moreover, an attention alignment mechanism is proposed to distill knowledge from the guidance of attributes to the pure-visual branch for samples without attributes. Extensive experiments and analysis show that our proposed module can significantly improve simple metric-based approaches to achieve state-of-the-art performance on different datasets and settings.