DOLG
Deep Orthogonal Fusion of Local and Global Features
Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their
local features. Previous learning-based studies mainly focus on either global or local image representation learning
to tackle the retrieval task. In this paper, we abandon the
two-stage paradigm and seek to design an effective singlestage solution by integrating local and global information
inside images into compact image representations. Specifically, we propose a Deep Orthogonal Local and Global
(DOLG) information fusion framework for end-to-end image retrieval. It attentively extracts representative local information with multi-atrous convolutions and self-attention
at first. Components orthogonal to the global image representation are then extracted from the local information.
At last, the orthogonal components are concatenated with
the global representation as a complementary, and then aggregation is performed to generate the final representation.
The whole framework is end-to-end differentiable and can
be trained with image-level labels. Extensive experimental
results validate the effectiveness of our solution and show
that our model achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets.
DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
https://paperswithcode.com/paper/dolg-single-stage-image-retrieval-with-deep
DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features