# Learning Attribute-Aware Hash Codes for Fine-Grained Image Retrieval via Query Optimization

arXiv: https://arxiv.org/abs/2509.17049 · HTML: https://mlsanity.com/paper/2509.17049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 56 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 30 of 32)

- [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)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [Fine-Grained Image Analysis with Deep Learning: A Survey](https://mlsanity.com/paper/2111.06119/) (2021)
- [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)
- [End-to-End Object Detection with Fully Convolutional Network](https://mlsanity.com/paper/2012.03544/) (2020)
- [Imputer: Sequence Modelling via Imputation and Dynamic Programming](https://mlsanity.com/paper/2002.08926/) (2020)
- [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)
- [One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective](https://mlsanity.com/paper/2109.14449/) (2021)
- [Asymmetric Deep Supervised Hashing](https://mlsanity.com/paper/1707.08325/) (2017)
- [RPC: A Large-Scale Retail Product Checkout Dataset](https://mlsanity.com/paper/1901.07249/) (2019)
- [Object Detection with Transformers: A Review](https://mlsanity.com/paper/2306.04670/) (2023)
- [ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval](https://mlsanity.com/paper/2008.01369/) (2020)
- [DeepSetNet: Predicting Sets with Deep Neural Networks](https://mlsanity.com/paper/1611.08998/) (2016)
- [D^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention](https://mlsanity.com/paper/2203.00860/) (2022)
- [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)
- [Elucidating image-to-set prediction: An analysis of models, losses and datasets](https://mlsanity.com/paper/1904.05709/) (2019)
- [Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval](https://mlsanity.com/paper/2311.12894/) (2023)
- [Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis](https://mlsanity.com/paper/2302.08706/) (2023)
- [Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2311.06067/) (2023)
- [Hashing with Binary Matrix Pursuit](https://mlsanity.com/paper/1808.01990/) (2018)
- [Learning Hash Functions Using Column Generation](https://mlsanity.com/paper/1303.0339/) (2013)

## Built on by (most cited first, 1 of 1)

- [Controlling Embedding Spaces with Text-Conditioned Transformations](https://mlsanity.com/paper/2607.22919/) (2026)

## Abstract (arXiv)

Fine-grained hashing has become a powerful solution for rapid and efficient image retrieval, particularly in scenarios requiring high discrimination between visually similar categories. To enable each hash bit to correspond to specific visual attributes, we propoe a novel method that harnesses learnable queries for attribute-aware hash codes learning. This method deploys a tailored set of queries to capture and represent nuanced attribute-level information within the hashing process, thereby enhancing both the interpretability and relevance of each hash bit. Building on this query-based optimization framework, we incorporate an auxiliary branch to help alleviate the challenges of complex landscape optimization often encountered with low-bit hash codes. This auxiliary branch models high-order attribute interactions, reinforcing the robustness and specificity of the generated hash codes. Experimental results on benchmark datasets demonstrate that our method generates attribute-aware hash codes and consistently outperforms state-of-the-art techniques in retrieval accuracy and robustness, especially for low-bit hash codes, underscoring its potential in fine-grained image hashing tasks.