LOCORE：基于长上下文序列建模的图像重排序
LOCORE: Image Re-ranking with Long-Context Sequence Modeling
AI总结：
LOCORE首次用局部描述符做列表式图像重排序，利用长上下文序列模型捕获依赖，在多个基准上性能更优且延迟与成对方法相当。
摘要：
我们引入了LOCORE（长上下文重排序器），该模型以图像查询对应的局部描述符和一组图库图像作为输入，并输出查询与每张图库图像之间的相似度分数。该模型用于图像检索，通常首先使用高效的相似性度量进行初步排序，然后基于更细粒度的相似性度量对排名靠前的短列表进行重排序。与现有方法（使用局部描述符进行成对相似性估计或使用全局描述符进行列表式重排序）相比，LOCORE是首个使用局部描述符进行列表式重排序的方法。为实现这一目标，我们利用高效的长上下文序列模型，在局部描述符级别有效捕获查询与图库图像之间的依赖关系。在测试阶段，我们采用滑动窗口策略处理长短列表，该策略专门设计用于克服序列模型上下文长度的限制。在公认的图像检索基准上，包括地标（ROxf和RPar）、产品（SOP）、时尚物品（In-Shop）和鸟类物种（CUB-200），我们的方法相比其他重排序器取得了更优的性能，同时延迟与成对局部描述符重排序器相当。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
