  Image retrieval systems conventionally use a two-stage paradigm, leveraging
global features for initial retrieval and local features for reranking.
However, the scalability of this method is often limited due to the significant
storage and computation cost incurred by local feature matching in the
reranking stage. In this paper, we present SuperGlobal, a novel approach that
exclusively employs global features for both stages, improving efficiency
without sacrificing accuracy. SuperGlobal introduces key enhancements to the
retrieval system, specifically focusing on the global feature extraction and
reranking processes. For extraction, we identify sub-optimal performance when
the widely-used ArcFace loss and Generalized Mean (GeM) pooling methods are
combined and propose several new modules to improve GeM pooling. In the
reranking stage, we introduce a novel method to update the global features of
the query and top-ranked images by only considering feature refinement with a
small set of images, thus being very compute and memory efficient. Our
experiments demonstrate substantial improvements compared to the state of the
art in standard benchmarks. Notably, on the Revisited Oxford+1M Hard dataset,
our single-stage results improve by 7.1%, while our two-stage gain reaches 3.7%
with a strong 64,865x speedup. Our two-stage system surpasses the current
single-stage state-of-the-art by 16.3%, offering a scalable, accurate
alternative for high-performing image retrieval systems with minimal time
overhead. Code: https://github.com/ShihaoShao-GH/SuperGlobal.
