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-# ECCV AI4DH 2024
+## Extended version of the DEArt dataset for object detection in artworks
+## Description
+**ECCV: European Conference on Computer Vision
+AI4DH 2024: 3rd International Workshop on Artificial Intelligence for Digital Humanities**
+This repository contains the materials presented in the paper 'An approach for dataset extension for object detection in artworks using open-vocabulary models'
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitlab.liris.cnrs.fr/anr-aaa/eccv-ai4dh-2024.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.liris.cnrs.fr/anr-aaa/eccv-ai4dh-2024/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
+<a href="https://liris.cnrs.fr/page-membre/tetiana-yemelianenko">Tetiana Yemelianenko</a>,
+<a href="https://liris.cnrs.fr/page-membre/iuliia-tkachenko">Iuliia Tkachenko</a>,
+<a href="https://liris.cnrs.fr/page-membre/tess-masclef">Tess Masclef</a>,
+<a href="https://liris.cnrs.fr/page-membre/mihaela-scuturici">Mihaela Scuturici</a>,
+<a href="https://liris.cnrs.fr/page-membre/serge-miguet">Serge Miguet</a>
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
+<div style="text-align:center"><img style="margin-right: 20px" src="assets/fig3.png" alt="Pipeline" height="325" width="660"/>
-## Name
-Choose a self-explaining name for your project.
+We provide the link to download the created extended version of DEArt dataset and the code for dataset extension.
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
+# Table of content
+- [Overview](#description)
+- [Dataset](#dataset)
+- [Steps for reproducing the process of dataset creation](#steps)
+- [Citation](#citation)
+- [Acknowledgements](#acknowledgments)
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
+## Dataset
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
+Extended dataset can be downloaded <a href="https://www.kaggle.com/datasets">here</a>, subset with new classes can be downloaded on the same page. Original DEArt dataset can be found here <a href="https://b2share.eudat.eu/records/449856a0b31543fc81baf19834f37d9d">DEArt</a>.
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
+Extended dataset is annotated in YOLO style, so for using the original version of the DEArt dataset and extended version you should convert annotations of DEArt dataet in YOLO style too.
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
+The new version of the dataset contains images from the 12th to 20th centuries in contrast with the original DEArt dataset with images from the 12th to 18th centuries. If it is necessary it is possible to restrict the period of the paintings by filtering images in the WikiArt dataset before dataset extension. In extended version images from WikiArt were used, so the new version contains not only paintings from European collections but also the paintings from Ukiyo-e - an ancient type of Japanese art, and others. If needed, you can create your own version of the dataset filtering styles by using shared code of the dataset creation.
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
+## Steps
+First you need to prepare two datasets. One small with the image-level annotations of classes which you plan to extend or add to the dataset, the second one - big non-annotated dataset from which we collect and annotate images on object level using proposed approach.
+Next you need to train YOLO model using the original dataset which you want to extend, calculate objectnesses of the objects for the images from the big non-annotated dataset, using OWL-ViT2, create index file for the objectnesses using ANNOY.
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
+To reproduce our steps you need finetuned on the original DEArt dataset YOLO model, file with calculated objectnesses for the images from Wikiart dataset and ANNOY index. These files are available upon a request.
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
+## Citation
+```
+@InProceedings{Yemelianenko_2024_ECCV,
+ author = {Yemelianenko, Tetiana and Tkachenko, Iuliia and Masclef, Tess and Scuturici, Mihaela and Miguet, Serge},
+ title = {An approach for dataset extension for object detection in artworks using open-vocabulary models},
+ booktitle = {},
+ month = {September},
+ year = {2024},
+ pages = {}
+}
+```
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
+## License
+The dataset is available under license Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) <a href="http://creativecommons.org/licenses/by-nc-sa/4.0">LiceRI</a>.
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+## Acknowledgments
+This work was funded by french national research agency with grant ANR-20-CE38-0017. We would like to thank the PAUSE ANR-Program: Ukrainian scientists support to support the scientific stay of T. Yemelianenko in LIRIS laboratory.
+<div style="text-align:center"><img style="margin-right: 20px" src="assets/logo_liris.png" alt="LIRIS logo" height="75" width="160"/>
+<div style="text-align:center"><img style="margin-right: 20px" src="assets/anr-logo.png" alt="ANR logo" height="75" width="160"/>
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-## License
-For open source projects, say how it is licensed.
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
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