# Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval

arXiv: https://arxiv.org/abs/2311.06067 · HTML: https://mlsanity.com/paper/2311.06067/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [HashNet: Deep Learning to Hash by Continuation](https://mlsanity.com/paper/1702.00758/) (2017)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks](https://mlsanity.com/paper/2105.02358/) (2021)
- [Feature Learning based Deep Supervised Hashing with Pairwise Labels](https://mlsanity.com/paper/1511.03855/) (2015)
- [Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval](https://mlsanity.com/paper/1604.04994/) (2016)
- [Asymmetric Deep Supervised Hashing](https://mlsanity.com/paper/1707.08325/) (2017)
- [Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval](https://mlsanity.com/paper/2007.15103/) (2020)
- [Triplet-Based Deep Hashing Network for Cross-Modal Retrieval](https://mlsanity.com/paper/1904.02449/) (2019)
- [ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval](https://mlsanity.com/paper/2008.01369/) (2020)
- [Deep Saliency Hashing](https://mlsanity.com/paper/1807.01459/) (2018)
- [SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval](https://mlsanity.com/paper/2209.13833/) (2022)
- [Deep Discrete Supervised Hashing](https://mlsanity.com/paper/1707.09905/) (2017)
- [A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2110.04479/) (2021)

## Built on by (most cited first, 5 of 5)

- [Learning Attribute-Aware Hash Codes for Fine-Grained Image Retrieval via Query Optimization](https://mlsanity.com/paper/2509.17049/) (2025)
- [UniHash: Unifying Pointwise and Pairwise Hashing Paradigms](https://mlsanity.com/paper/2601.09828/) (2026)
- [Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval](https://mlsanity.com/paper/2505.13907/) (2025)
- [RFHNet: Relational and Frequency-Aware Hashing Network for Large-Scale Fine-Grained Food Image Retrieval](https://mlsanity.com/paper/2607.06148/) (2026)
- [Controlling Embedding Spaces with Text-Conditioned Transformations](https://mlsanity.com/paper/2607.22919/) (2026)

## Abstract (arXiv)

In recent years, hashing methods have been popular in the large-scale media search for low storage and strong representation capabilities. To describe objects with similar overall appearance but subtle differences, more and more studies focus on hashing-based fine-grained image retrieval. Existing hashing networks usually generate both local and global features through attention guidance on the same deep activation tensor, which limits the diversity of feature representations. To handle this limitation, we substitute convolutional descriptors for attention-guided features and propose an Attributes Grouping and Mining Hashing (AGMH), which groups and embeds the category-specific visual attributes in multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval. Specifically, an Attention Dispersion Loss (ADL) is designed to force the descriptors to attend to various local regions and capture diverse subtle details. Moreover, we propose a Stepwise Interactive External Attention (SIEA) to mine critical attributes in each descriptor and construct correlations between fine-grained attributes and objects. The attention mechanism is dedicated to learning discrete attributes, which will not cost additional computations in hash codes generation. Finally, the compact binary codes are learned by preserving pairwise similarities. Experimental results demonstrate that AGMH consistently yields the best performance against state-of-the-art methods on fine-grained benchmark datasets.