7 citations · 8 across the 3 of their papers we have counts for
3 papers
Connecting Images through Time and Sources: Introducing Low-data, Heterogeneous Instance Retrieval
Dimitri Gominski, Valérie Gouet-Brunet, Liming Chen
With impressive results in applications relying on feature learning, deep learning has also blurred the line between algorithm and data. Pick a training dataset, pick a backbone ne…
Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning
Dimitri Gominski, Valérie Gouet-Brunet, Liming Chen
Advances in high resolution remote sensing image analysis are currently hampered by the difficulty of gathering enough annotated data for training deep learning methods, giving ris…
Challenging deep image descriptors for retrieval in heterogeneous iconographic collections
Dimitri Gominski, Martyna Poreba, Valérie Gouet-Brunet +1
This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of comp…