Deep Learning for Historical Data Analysis-Reference-Cited by-同舟云学术

Deep Learning for Historical Data Analysis

Published:2021-10-20 Issue: Volume: Page:
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Container-title:Proceedings of the 3rd Workshop on Structuring and Understanding of Multimedia heritAge Contents
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Author:

Aubry Mathieu¹

Affiliation:

1. LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, Marne la Vallee, France

Funder

Agence Nationale de la recherche (ANR)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3475720.3476877

Reference7 articles.

1. Image Collation: Matching Illustrations in Manuscripts

2. Tom Monnier and Mathieu Aubry. 2020. docExtractor: An off-the-shelf historical document element extraction. In ICFHR . Tom Monnier and Mathieu Aubry. 2020. docExtractor: An off-the-shelf historical document element extraction. In ICFHR .

3. Tom Monnier Thibault Groueix and Mathieu Aubry. 2020. Deep Transformation-Invariant Clustering. In NeurIPS . Tom Monnier Thibault Groueix and Mathieu Aubry. 2020. Deep Transformation-Invariant Clustering. In NeurIPS .

4. Tom Monnier Elliot Vincent Jean Ponce and Mathieu Aubry. 2021. Unsupervised Layered Image Decomposition into Object Prototypes. In ICCV . Tom Monnier Elliot Vincent Jean Ponce and Mathieu Aubry. 2021. Unsupervised Layered Image Decomposition into Object Prototypes. In ICCV .

5. RANSAC-Flow: Generic Two-Stage Image Alignment

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Historical Document Image Segmentation Combining Deep Learning and Gabor Features;Lecture Notes in Computer Science;2023

2. An End-to-End Framework for Evaluating Explainable Deep Models: Application to Historical Document Image Segmentation;Computational Collective Intelligence;2022