activity
20192022
most citedChallenging deep image descriptors for retrieval in heterogeneous iconographic collections

7 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2022

Location retrieval using visible landmarks based qualitative place signatures

Lijun Wei, Valerie Gouet-Brunet, Anthony Cohn

Location retrieval based on visual information is to retrieve the location of an agent (e.g. human, robot) or the area they see by comparing the observations with a certain form of…

cs.CV2021

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…

cs.CV2021★ 1 cited

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…

cs.CV2020★ 6 cited

Semantic Signatures for Large-scale Visual Localization

Li Weng, Valerie Gouet-Brunet, Bahman Soheilian

Visual localization is a useful alternative to standard localization techniques. It works by utilizing cameras. In a typical scenario, features are extracted from captured images a…

cs.CV2019★ 7 cited

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…