LOCORE: Image Re-ranking with Long-Context Sequence Modeling

We introduce LOCORE, Long-Context Re-ranker, a model that takes as input local descriptors corresponding to an image query and a list of gallery images and outputs similarity scores between the query and each gallery image. This model is used for image retrieval, where typically a first ranking is performed with an efficient similarity measure, and then a shortlist of top-ranked images is re-ranked based on a more fine-grained similarity measure. Compared to existing methods that perform pair-wise similarity estimation with local descriptors or list-wise re-ranking with global descriptors, LOCORE is the first method to perform list-wise re-ranking with local descriptors. To achieve this, we leverage efficient long-context sequence models to effectively capture the dependencies between query and gallery images at the local-descriptor level. During testing, we process long shortlists with a sliding window strategy that is tailored to overcome the context size limitations of sequence models. Our approach achieves superior performance compared with other re-rankers on established image retrieval benchmarks of landmarks (ROxf and RPar), products (SOP), fashion items (In-Shop), and bird species (CUB-200) while having comparable latency to the pair-wise local descriptor re-rankers.

@article{xiao2025_2503.21772,
  title={ LOCORE: Image Re-ranking with Long-Context Sequence Modeling },
  author={ Zilin Xiao and Pavel Suma and Ayush Sachdeva and Hao-Jen Wang and Giorgos Kordopatis-Zilos and Giorgos Tolias and Vicente Ordonez },
  journal={arXiv preprint arXiv:2503.21772},
  year={ 2025 }
}