# Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval

arXiv: https://arxiv.org/abs/2301.13014 · HTML: https://mlsanity.com/paper/2301.13014/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 60 references that are in the map.
- First posted on arXiv 27 Dec 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Exploring Self-attention for Image Recognition](https://mlsanity.com/paper/2004.13621/) (2020)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Scaling Local Self-Attention for Parameter Efficient Visual Backbones](https://mlsanity.com/paper/2103.12731/) (2021)
- [Automatic Spatially-aware Fashion Concept Discovery](https://mlsanity.com/paper/1708.01311/) (2017)
- [Attention in Natural Language Processing](https://mlsanity.com/paper/1902.02181/) (2019)
- [Learning Fashion Compatibility with Bidirectional LSTMs](https://mlsanity.com/paper/1707.05691/) (2017)
- [Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network](https://mlsanity.com/paper/1505.07922/) (2015)
- [Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention](https://mlsanity.com/paper/2010.07891/) (2020)
- [Exploring Human-like Attention Supervision in Visual Question Answering](https://mlsanity.com/paper/1709.06308/) (2017)
- [Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid](https://mlsanity.com/paper/1908.11754/) (2019)
- [Multi-Pointer Co-Attention Networks for Recommendation](https://mlsanity.com/paper/1801.09251/) (2018)
- [Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network](https://mlsanity.com/paper/2002.02814/) (2020)
- [Cross-Domain Image Retrieval with Attention Modeling](https://mlsanity.com/paper/1709.01784/) (2017)
- [Learning Diverse Fashion Collocation by Neural Graph Filtering](https://mlsanity.com/paper/2003.04888/) (2020)
- [MMFashion: An Open-Source Toolbox for Visual Fashion Analysis](https://mlsanity.com/paper/2005.08847/) (2020)
- [Clothing Retrieval with Visual Attention Model](https://mlsanity.com/paper/1710.11446/) (2017)
- [Query-free Clothing Retrieval via Implicit Relevance Feedback](https://mlsanity.com/paper/1711.00248/) (2017)
- [Training and challenging models for text-guided fashion image retrieval](https://mlsanity.com/paper/2204.11004/) (2022)
- [SAT: Self-adaptive training for fashion compatibility prediction](https://mlsanity.com/paper/2206.12622/) (2022)

## Built on by (most cited first, 3 of 3)

- [Human-MME: A Holistic Evaluation Benchmark for Human-Centric Multimodal Large Language Models](https://mlsanity.com/paper/2509.26165/) (2025)
- [A Multihead Continual Learning Framework for Fine-Grained Fashion Image Retrieval with Contrastive Learning and Exponential Moving Average Distillation](https://mlsanity.com/paper/2603.20648/) (2026)
- [Garment Attribute Manipulation with Multi-level Attention](https://mlsanity.com/paper/2409.10206/) (2024)

## Abstract (arXiv)

Fine-grained fashion retrieval searches for items that share a similar attribute with the query image. Most existing methods use a pre-trained feature extractor (e.g., ResNet 50) to capture image representations. However, a pre-trained feature backbone is typically trained for image classification and object detection, which are fundamentally different tasks from fine-grained fashion retrieval. Therefore, existing methods suffer from a feature gap problem when directly using the pre-trained backbone for fine-tuning. To solve this problem, we introduce an attribute-guided multi-level attention network (AG-MAN). Specifically, we first enhance the pre-trained feature extractor to capture multi-level image embedding, thereby enriching the low-level features within these representations. Then, we propose a classification scheme where images with the same attribute, albeit with different values, are categorized into the same class. This can further alleviate the feature gap problem by perturbing object-centric feature learning. Moreover, we propose an improved attribute-guided attention module for extracting more accurate attribute-specific representations. Our model consistently outperforms existing attention based methods when assessed on the FashionAI (62.8788% in MAP), DeepFashion (8.9804% in MAP), and Zappos50k datasets (93.32% in Prediction accuracy). Especially, ours improves the most typical ASENet_V2 model by 2.12%, 0.31%, and 0.78% points in FashionAI, DeepFashion, and Zappos50k datasets, respectively. The source code is available in https://github.com/Dr-LingXiao/AG-MAN.