Object Retrieval and Localization in Large Art Collections Using Deep Multi-style Feature Fusion and Iterative Voting-Reference-Cited by-同舟云学术

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Object Retrieval and Localization in Large Art Collections Using Deep Multi-style Feature Fusion and Iterative Voting

Published:2020 Issue: Volume: Page:159-176
ISSN:0302-9743
Container-title:Computer Vision – ECCV 2020 Workshops
language:
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Author:

Ufer Nikolai,Lang Sabine,Ommer Björn

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-66096-3_12

Reference70 articles.

1. Artuk. https://artuk.org. Accessed 31 May 2020

2. “brueghel family: Jan brueghel the elder.” the brueghel family database. university of california, berkeley. http://www.janbrueghel.net. Accessed 16 Oct 2018

3. WGA. https://wga.hu. Accessed 31 May 2020

4. Wikiart. https://www.wikiart.org. Accessed 16 Oct 2018

5. Lecture Notes in Computer Science;SA Amirshahi,2015

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1. ST-SACLF: Style Transfer Informed Self-attention Classifier for Bias-Aware Painting Classification;Communications in Computer and Information Science;2024

2. Large-scale interactive retrieval in art collections using multi-style feature aggregation;PLOS ONE;2021-11-24

3. Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview;Neural Computing and Applications;2021-04-02

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