Unifying Deep Local and Global Features for Image Search

396 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

CrossTransformers: spatially-aware few-shot transfer (2020, background): “Discriminative parts [4, 9, 22, 34, 42, 83] and visual words [74, 101] have a rich history, and have found applications in deformable-parts models [26], classification [70, 101], and retrieval [12, 84].”
Google Landmarks Dataset v2 – A Large-Scale Benchmark for Instance-Level Recognition and Retrieval (2020, methodology): “3, where we compare against models trained on other datasets, as well as recent state-of-the-art results – including methods based on global descriptors [44, 45], local feature aggregation [43, 53] and uniﬁed global+local features [10].”
Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation (2021, background): “Part-level features have been more widely used in the domain of image retrieval with both classical [34, 5] and deep learning based [2, 37, 42, 45, 7] methods.”