# Instance-Aware Hashing for Multi-Label Image Retrieval

arXiv: https://arxiv.org/abs/1603.03234 · HTML: https://mlsanity.com/paper/1603.03234/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2016.

## Built on by (most cited first, 10 of 10)

- [Instance-weighted Central Similarity for Multi-label Image Retrieval](https://mlsanity.com/paper/2108.05274/) (2021)
- [Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2205.13092/) (2022)
- [HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval](https://mlsanity.com/paper/2212.08281/) (2022)
- [Deep Partial Multi-Label Learning with Graph Disambiguation](https://mlsanity.com/paper/2305.05882/) (2023)
- [Rescuing Deep Hashing from Dead Bits Problem](https://mlsanity.com/paper/2102.00648/) (2021)
- [HyP2 Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval](https://mlsanity.com/paper/2208.06866/) (2022)
- [Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search](https://mlsanity.com/paper/2102.01486/) (2021)
- [Learning Semantic-Aware Representation in Visual-Language Models for Multi-Label Recognition with Partial Labels](https://mlsanity.com/paper/2412.10843/) (2024)
- [Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning](https://mlsanity.com/paper/2505.20938/) (2025)
- [Learning Semantic-Aware Threshold for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2507.23263/) (2025)

## Abstract (arXiv)

Similarity-preserving hashing is a commonly used method for nearest neighbour search in large-scale image retrieval. For image retrieval, deep-networks-based hashing methods are appealing since they can simultaneously learn effective image representations and compact hash codes. This paper focuses on deep-networks-based hashing for multi-label images, each of which may contain objects of multiple categories. In most existing hashing methods, each image is represented by one piece of hash code, which is referred to as semantic hashing. This setting may be suboptimal for multi-label image retrieval. To solve this problem, we propose a deep architecture that learns instance-aware image representations for multi-label image data, which are organized in multiple groups, with each group containing the features for one category. The instance-aware representations not only bring advantages to semantic hashing, but also can be used in category-aware hashing, in which an image is represented by multiple pieces of hash codes and each piece of code corresponds to a category. Extensive evaluations conducted on several benchmark datasets demonstrate that, for both semantic hashing and category-aware hashing, the proposed method shows substantial improvement over the state-of-the-art supervised and unsupervised hashing methods.