# Enhancing Visual Re-ranking through Denoising Nearest Neighbor Graph via Continuous CRF

arXiv: https://arxiv.org/abs/2412.13875 · HTML: https://mlsanity.com/paper/2412.13875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking](https://mlsanity.com/paper/1803.11285/) (2018)
- [Deep Image Retrieval: Learning global representations for image search](https://mlsanity.com/paper/1604.01325/) (2016)
- [Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations](https://mlsanity.com/paper/1611.05113/) (2016)
- [Local Features and Visual Words Emerge in Activations](https://mlsanity.com/paper/1905.06358/) (2019)
- [Recent Advance in Content-based Image Retrieval: A Literature Survey](https://mlsanity.com/paper/1706.06064/) (2017)
- [Attention-Based Query Expansion Learning](https://mlsanity.com/paper/2007.08019/) (2020)
- [Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing](https://mlsanity.com/paper/1811.10907/) (2018)
- [Contextual Similarity Aggregation with Self-attention for Visual Re-ranking](https://mlsanity.com/paper/2110.13430/) (2021)
- [Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval](https://mlsanity.com/paper/1908.06896/) (2019)
- [Topological RANSAC for instance verification and retrieval without fine-tuning](https://mlsanity.com/paper/2310.06486/) (2023)

## Abstract (arXiv)

Nearest neighbor (NN) graph based visual re-ranking has emerged as a powerful approach for improving retrieval accuracy, offering the advantages of effectively exploring high-dimensional manifolds without requiring additional fine-tuning. However, the effectiveness of NN graph-based re-ranking is fundamentally constrained by the quality of its edge connectivity, as incorrect connections between dissimilar (negative) images frequently occur. This is known as a noisy edge problem, which hinders the re-ranking performance of existing techniques and limits their potential. To remedy this issue, we propose a complementary denoising method based on Continuous Conditional Random Fields (C-CRF) that leverages statistical distances derived from similarity-based distributions. As a pre-processing step for enhancing NN graph-based retrieval, our approach constructs fully connected cliques around each anchor image and employs a novel statistical distance metric to robustly alleviate noisy edges before re-ranking while achieving efficient processing through offline computation. Extensive experimental results demonstrate that our method consistently improves three different NN graph-based re-ranking approaches, yielding significant gains in retrieval accuracy.