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Found 7 papers, 4 papers with code
TLDR: Twin Learning for Dimensionality Reduction
Dimensionality reduction methods are unsupervised approaches which learn low-dimensional spaces where some properties of the initial space, typically the notion of "neighborhood", are preserved.
Learning with Average Precision: Training Image Retrieval with a Listwise Loss
Recent deep models for image retrieval have outperformed traditional methods by leveraging ranking-tailored loss functions, but important theoretical and practical problems remain.
Re-ID done right: towards good practices for person re-identification
In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification.
End-to-end Learning of Deep Visual Representations for Image Retrieval
Second, we build on the recent R-MAC descriptor, show that it can be interpreted as a deep and differentiable architecture, and present improvements to enhance it.
Ranked #13 on
Image Retrieval
on ROxford (Medium)
Deep Image Retrieval: Learning global representations for image search
We propose a novel approach for instance-level image retrieval.
Ranked #3 on
Image Retrieval
on Oxf105k
What is the right way to represent document images?
In this article we study the problem of document image representation based on visual features.
LEWIS: Latent Embeddings for Word Images and their Semantics
The goal of this work is to bring semantics into the tasks of text recognition and retrieval in natural images.
