# Attribute-Aware Attention Model for Fine-grained Representation Learning

arXiv: https://arxiv.org/abs/1901.00392 · HTML: https://mlsanity.com/paper/1901.00392/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2019.

## Built on by (most cited first, 22 of 22)

- [Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark](https://mlsanity.com/paper/2306.02898/) (2023)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [TSA-Net: Tube Self-Attention Network for Action Quality Assessment](https://mlsanity.com/paper/2201.03746/) (2022)
- [GhostNets on Heterogeneous Devices via Cheap Operations](https://mlsanity.com/paper/2201.03297/) (2022)
- [Graph-based Person Signature for Person Re-Identifications](https://mlsanity.com/paper/2104.06770/) (2021)
- [Towards Fine-grained Visual Representations by Combining Contrastive Learning with Image Reconstruction and Attention-weighted Pooling](https://mlsanity.com/paper/2104.04323/) (2021)
- [Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition](https://mlsanity.com/paper/2107.06538/) (2021)
- [Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search](https://mlsanity.com/paper/2411.17776/) (2024)
- [Video Person Re-identification using Attribute-enhanced Features](https://mlsanity.com/paper/2108.06946/) (2021)
- [PARTICUL: Part Identification with Confidence measure using Unsupervised Learning](https://mlsanity.com/paper/2206.13304/) (2022)
- [AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes](https://mlsanity.com/paper/2308.07537/) (2023)
- [DVF: Advancing Robust and Accurate Fine-Grained Image Retrieval with Retrieval Guidelines](https://mlsanity.com/paper/2404.15771/) (2024)
- [Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph](https://mlsanity.com/paper/2303.15266/) (2023)
- [Predicting Scores of Various Aesthetic Attribute Sets by Learning from Overall Score Labels](https://mlsanity.com/paper/2312.03222/) (2023)
- [Attribute-aware Explainable Complementary Clothing Recommendation](https://mlsanity.com/paper/2107.01655/) (2021)
- [Interpretable Out-Of-Distribution Detection Using Pattern Identification](https://mlsanity.com/paper/2302.10303/) (2023)
- [Cerberus: Attribute-based person re-identification using semantic IDs](https://mlsanity.com/paper/2412.01048/) (2024)
- [Fine-grained Image Retrieval via Dual-Vision Adaptation](https://mlsanity.com/paper/2506.16273/) (2025)
- [Attribute Guided Sparse Tensor-Based Model for Person Re-Identification](https://mlsanity.com/paper/2108.04352/) (2021)
- [CBIR using Pre-Trained Neural Networks](https://mlsanity.com/paper/2110.14455/) (2021)
- [Zero-shot Classification using Hyperdimensional Computing](https://mlsanity.com/paper/2401.16876/) (2024)
- [Beyond Visual Cues: Semantic-Driven Token Filtering and Expert Routing for Anytime Person ReID](https://mlsanity.com/paper/2604.15090/) (2026)

## Abstract (arXiv)

How to learn a discriminative fine-grained representation is a key point in many computer vision applications, such as person re-identification, fine-grained classification, fine-grained image retrieval, etc. Most of the previous methods focus on learning metrics or ensemble to derive better global representation, which are usually lack of local information. Based on the considerations above, we propose a novel Attribute-Aware Attention Model (A3M), which can learn local attribute representation and global category representation simultaneously in an end-to-end manner. The proposed model contains two attention models: attribute-guided attention module uses attribute information to help select category features in different regions, at the same time, category-guided attention module selects local features of different attributes with the help of category cues. Through this attribute-category reciprocal process, local and global features benefit from each other. Finally, the resulting feature contains more intrinsic information for image recognition instead of the noisy and irrelevant features. Extensive experiments conducted on Market-1501, CompCars, CUB-200-2011 and CARS196 demonstrate the effectiveness of our A3M. Code is available at https://github.com/iamhankai/attribute-aware-attention.