Global Features are All You Need for Image Retrieval and Reranking

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.