Utilizing a two-stage paradigm comprising of coarse image retrieval and precise reranking, a well-established image retrieval system is formed. It has been widely accepted for long time that local feature is imperative to the subsequent stage - reranking, but this requires sizeable storage and computing capacities. We, for the first time, propose an image retrieval paradigm leveraging global feature only to enable accurate and lightweight image retrieval for both coarse retrieval and reranking, thus the name - SuperGlobal. It consists of several plug-in modules that can be easily integrated into an already trained model, for both coarse retrieval and reranking stage. This series of approaches is inspired by the investigation into Generalized Mean (GeM) Pooling. Possessing these tools, we strive to defy the notion that local feature is essential for a high-performance image retrieval paradigm. Extensive experiments demonstrate substantial improvements compared to the state of the art in standard benchmarks. Notably, on the Revisited Oxford (ROxford)+1M Hard dataset, our single-stage results improve by 8.2% absolute, while our two-stage version gain reaches 3.7% with a strong 7568X speedup. Furthermore, when the full SuperGlobal is compared with the current single-stage state-of-the-art method, we achieve roughly 17% improvement with a minimal 0.005% time overhead. Code: https://github.com/ShihaoShao-GH/SuperGlobal.

翻译：采用由粗粒度图像检索与精确重排序组成的双阶段范式，构建了完善的图像检索系统。长期以来，局部特征被认为是后续重排序阶段不可或缺的元素，但这需要庞大的存储与计算资源。我们首次提出了一种仅利用全局特征的图像检索范式，在粗粒度检索与重排序阶段均能实现精确且轻量级的图像检索，故命名为SuperGlobal。该范式包含多个即插即用模块，可轻松集成至已训练模型中，同时服务于粗粒度检索与重排序阶段。这一系列方法受广义均值（GeM）池化的研究启发。借助这些工具，我们力图挑战“高性能图像检索范式必须依赖局部特征”的传统认知。大量实验表明，该方法在标准基准测试中相较现有最优方案有显著提升。值得注意的是，在Revisited Oxford (ROxford)+1M Hard数据集上，单阶段结果绝对提升8.2%，而双阶段版本在实现7568倍加速的同时获得3.7%的提升。此外，将完整SuperGlobal与当前单阶段最优方法对比，我们仅以0.005%的极低时间开销实现了约17%的性能提升。代码地址：https://github.com/ShihaoShao-GH/SuperGlobal。