# Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing

arXiv: https://arxiv.org/abs/1811.10907 · HTML: https://mlsanity.com/paper/1811.10907/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2018.

## Built on by (most cited first, 20 of 20)

- [Deep Learning for Instance Retrieval: A Survey](https://mlsanity.com/paper/2101.11282/) (2021)
- [Part-based Pseudo Label Refinement for Unsupervised Person Re-identification](https://mlsanity.com/paper/2203.14675/) (2022)
- [Viewpoint Invariant Dense Matching for Visual Geolocalization](https://mlsanity.com/paper/2109.09827/) (2021)
- [Boosting vision transformers for image retrieval](https://mlsanity.com/paper/2210.11909/) (2022)
- [All the attention you need: Global-local, spatial-channel attention for image retrieval](https://mlsanity.com/paper/2107.08000/) (2021)
- [Effectively Leveraging Attributes for Visual Similarity](https://mlsanity.com/paper/2105.01695/) (2021)
- [Evaluating Contrastive Models for Instance-based Image Retrieval](https://mlsanity.com/paper/2104.14939/) (2021)
- [DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy](https://mlsanity.com/paper/2407.17272/) (2024)
- [On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection](https://mlsanity.com/paper/2510.10456/) (2025)
- [Instance Image Retrieval by Learning Purely From Within the Dataset](https://mlsanity.com/paper/2208.06119/) (2022)
- [Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning](https://mlsanity.com/paper/2304.12448/) (2023)
- [Random Walk on Pixel Manifolds for Anomaly Segmentation of Complex Driving Scenes](https://mlsanity.com/paper/2404.17961/) (2024)
- [Cluster-Aware Similarity Diffusion for Instance Retrieval](https://mlsanity.com/paper/2406.02343/) (2024)
- [Connecting Images through Time and Sources: Introducing Low-data, Heterogeneous Instance Retrieval](https://mlsanity.com/paper/2103.10729/) (2021)
- [Indicative Image Retrieval: Turning Blackbox Learning into Grey](https://mlsanity.com/paper/2201.11898/) (2022)
- [Self-distillation with Online Diffusion on Batch Manifolds Improves Deep Metric Learning](https://mlsanity.com/paper/2211.07566/) (2022)
- [MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval](https://mlsanity.com/paper/2303.03050/) (2023)
- [Enhancing Visual Re-ranking through Denoising Nearest Neighbor Graph via Continuous CRF](https://mlsanity.com/paper/2412.13875/) (2024)
- [Locality Preserving Markovian Transition for Instance Retrieval](https://mlsanity.com/paper/2506.05196/) (2025)
- [3rd Place Solution to ICCV LargeFineFoodAI Retrieval](https://mlsanity.com/paper/2510.21198/) (2025)

## Abstract (arXiv)

Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years. A downside to diffusion is that it performs slowly in comparison to the naive k-NN search, which causes a non-trivial online computational cost on large datasets. To overcome this weakness, we propose a novel diffusion technique in this paper. In our work, instead of applying diffusion to the query, we pre-compute the diffusion results of each element in the database, making the online search a simple linear combination on top of the k-NN search process. Our proposed method becomes 10~ times faster in terms of online search speed. Moreover, we propose to use late truncation instead of early truncation in previous works to achieve better retrieval performance.