# Central Similarity Quantization for Efficient Image and Video Retrieval

arXiv: https://arxiv.org/abs/1908.00347 · HTML: https://mlsanity.com/paper/1908.00347/

## Key facts

- Cited by 77 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 30 of 77)

- [Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning](https://mlsanity.com/paper/2101.08482/) (2021)
- [Efficient Video Transformers with Spatial-Temporal Token Selection](https://mlsanity.com/paper/2111.11591/) (2021)
- [One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective](https://mlsanity.com/paper/2109.14449/) (2021)
- [Self-supervised Product Quantization for Deep Unsupervised Image Retrieval](https://mlsanity.com/paper/2109.02244/) (2021)
- [BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label](https://mlsanity.com/paper/2207.00278/) (2022)
- [GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation](https://mlsanity.com/paper/2303.14655/) (2023)
- [Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery](https://mlsanity.com/paper/2410.19213/) (2024)
- [DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval](https://mlsanity.com/paper/2106.13266/) (2021)
- [Continual Learning for Visual Search with Backward Consistent Feature Embedding](https://mlsanity.com/paper/2205.13384/) (2022)
- [Contrastive Quantization with Code Memory for Unsupervised Image Retrieval](https://mlsanity.com/paper/2109.05205/) (2021)
- [COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing](https://mlsanity.com/paper/2402.16424/) (2024)
- [OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution](https://mlsanity.com/paper/2108.03541/) (2021)
- [Self-Supervised Video Similarity Learning](https://mlsanity.com/paper/2304.03378/) (2023)
- [HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning](https://mlsanity.com/paper/2212.00325/) (2022)
- [Vision Transformer Hashing for Image Retrieval](https://mlsanity.com/paper/2109.12564/) (2021)
- [Deep Hash Distillation for Image Retrieval](https://mlsanity.com/paper/2112.08816/) (2021)
- [One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching](https://mlsanity.com/paper/2205.15721/) (2022)
- [Hyperbolic Hierarchical Contrastive Hashing](https://mlsanity.com/paper/2212.08904/) (2022)
- [Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval](https://mlsanity.com/paper/2311.12894/) (2023)
- [VPN: Video Provenance Network for Robust Content Attribution](https://mlsanity.com/paper/2109.10038/) (2021)
- [Instance-weighted Central Similarity for Multi-label Image Retrieval](https://mlsanity.com/paper/2108.05274/) (2021)
- [NeuroHash: A Hyperdimensional Neuro-Symbolic Framework for Spatially-Aware Image Hashing and Retrieval](https://mlsanity.com/paper/2404.11025/) (2024)
- [DarkHash: A Data-Free Backdoor Attack Against Deep Hashing](https://mlsanity.com/paper/2510.08094/) (2025)
- [Unsupervised Hashing with Semantic Concept Mining](https://mlsanity.com/paper/2209.11475/) (2022)
- [Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval](https://mlsanity.com/paper/2310.14637/) (2023)
- [Rescuing Deep Hashing from Dead Bits Problem](https://mlsanity.com/paper/2102.00648/) (2021)
- [3D-CSL: self-supervised 3D context similarity learning for Near-Duplicate Video Retrieval](https://mlsanity.com/paper/2211.05352/) (2022)
- [PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained Image Retrieval](https://mlsanity.com/paper/2109.05206/) (2021)
- [HyP2 Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval](https://mlsanity.com/paper/2208.06866/) (2022)
- [Central Similarity Multi-View Hashing for Multimedia Retrieval](https://mlsanity.com/paper/2308.13774/) (2023)

## Abstract (arXiv)

Existing data-dependent hashing methods usually learn hash functions from pairwise or triplet data relationships, which only capture the data similarity locally, and often suffer from low learning efficiency and low collision rate. In this work, we propose a new global similarity metric, termed as central similarity, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy. We principally formulate the computation of the proposed central similarity metric by introducing a new concept, i.e., hash center that refers to a set of data points scattered in the Hamming space with a sufficient mutual distance between each other. We then provide an efficient method to construct well separated hash centers by leveraging the Hadamard matrix and Bernoulli distributions. Finally, we propose the Central Similarity Quantization (CSQ) that optimizes the central similarity between data points w.r.t. their hash centers instead of optimizing the local similarity. CSQ is generic and applicable to both image and video hashing scenarios. Extensive experiments on large-scale image and video retrieval tasks demonstrate that CSQ can generate cohesive hash codes for similar data pairs and dispersed hash codes for dissimilar pairs, achieving a noticeable boost in retrieval performance, i.e. 3%-20% in mAP over the previous state-of-the-arts. The code is at: urlhttps://github.com/yuanli2333/Hadamard-Matrix-for-hashing