2 datasets found
  1. t

    Semantic Image-Text-Classes - Vdataset - LDM

    • service.tib.eu
    Updated Apr 23, 2019
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    (2019). Semantic Image-Text-Classes - Vdataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/luh-image-text-classes
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    Dataset updated
    Apr 23, 2019
    License

    Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
    License information was derived automatically

    Description

    This dataset is introduced by the paper "Understanding, Categorizing and Predicting Semantic Image-Text Relations". If you are using this dataset it in your work, please cite: @inproceedings{otto2019understanding, title={Understanding, Categorizing and Predicting Semantic Image-Text Relations}, author={Otto, Christian and Springstein, Matthias and Anand, Avishek and Ewerth, Ralph}, booktitle={In Proceedings of ACM International Conference on Multimedia Retrieval (ICMR 2019)}, year={2019} } To create the full tar use the following command in the command line: cat train.tar.part* > train_concat.tar Then simply untar it via tar -xf train_concat.tar The jsonl files contain metadata of the following format: id, origin, CMI, SC, STAT, ITClass, text, tagged text, image_path License Information: This dataset is composed of various open access sources as described in the paper. We thank all the original authors for their work. Pitt Image Ads Dataset: http://people.cs.pitt.edu/~kovashka/ads/ Image-Net challenge: http://image-net.org/ Visual Storytelling Dataset (VIST): http://visionandlanguage.net/VIST/ Wikipedia: https://www.wikipedia.org/ Microsoft COCO: http://cocodataset.org/#home

  2. F

    Semantic Image-Text-Classes

    • data.uni-hannover.de
    jsonl, partaa, partab +48
    Updated Jan 20, 2022
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    TIB (2022). Semantic Image-Text-Classes [Dataset]. https://data.uni-hannover.de/dataset/image-text-classes
    Explore at:
    partar(1000000000), partbb(1000000000), partae(1000000000), partaq(1000000000), partau(1000000000), partam(1000000000), partbl(1000000000), partbo(1000000000), partab(1000000000), partai(1000000000), partbk(1000000000), partbw(532254720), partbu(1000000000), partbf(1000000000), partbn(1000000000), partas(1000000000), partad(1000000000), partbr(1000000000), partao(1000000000), partbv(1000000000), partaa(1000000000), partav(1000000000), partbe(1000000000), partbq(1000000000), partay(1000000000), jsonl(145621225), partax(1000000000), partap(1000000000), partaj(1000000000), partbd(1000000000), partbs(1000000000), partaz(1000000000), partbp(1000000000), partaw(1000000000), partah(1000000000), partbh(1000000000), tar(163174400), partaf(1000000000), partan(1000000000), partbi(1000000000), partbt(1000000000), partba(1000000000), partbm(1000000000), partbc(1000000000), partbj(1000000000), partat(1000000000), jsonl(1161897), partbg(1000000000), partal(1000000000), partac(1000000000), partag(1000000000), partak(1000000000)Available download formats
    Dataset updated
    Jan 20, 2022
    Dataset authored and provided by
    TIB
    License

    Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
    License information was derived automatically

    Description

    This dataset is introduced by the paper "Understanding, Categorizing and Predicting Semantic Image-Text Relations".

    If you are using this dataset it in your work, please cite:

    @inproceedings{otto2019understanding,
title={Understanding, Categorizing and Predicting Semantic Image-Text Relations},
author={Otto, Christian and Springstein, Matthias and Anand, Avishek and Ewerth, Ralph},
booktitle={In Proceedings of ACM International Conference on Multimedia Retrieval (ICMR 2019)},
year={2019}
}

    To create the full tar use the following command in the command line:

    cat train.tar.part* > train_concat.tar

    Then simply untar it via

    tar -xf train_concat.tar

    The jsonl files contain metadata of the following format:

    id, origin, CMI, SC, STAT, ITClass, text, tagged text, image_path

    License Information:

    This dataset is composed of various open access sources as described in the paper. We thank all the original authors for their work.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
(2019). Semantic Image-Text-Classes - Vdataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/luh-image-text-classes

Semantic Image-Text-Classes - Vdataset - LDM

Explore at:
Dataset updated
Apr 23, 2019
License

Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
License information was derived automatically

Description

This dataset is introduced by the paper "Understanding, Categorizing and Predicting Semantic Image-Text Relations". If you are using this dataset it in your work, please cite: @inproceedings{otto2019understanding, title={Understanding, Categorizing and Predicting Semantic Image-Text Relations}, author={Otto, Christian and Springstein, Matthias and Anand, Avishek and Ewerth, Ralph}, booktitle={In Proceedings of ACM International Conference on Multimedia Retrieval (ICMR 2019)}, year={2019} } To create the full tar use the following command in the command line: cat train.tar.part* > train_concat.tar Then simply untar it via tar -xf train_concat.tar The jsonl files contain metadata of the following format: id, origin, CMI, SC, STAT, ITClass, text, tagged text, image_path License Information: This dataset is composed of various open access sources as described in the paper. We thank all the original authors for their work. Pitt Image Ads Dataset: http://people.cs.pitt.edu/~kovashka/ads/ Image-Net challenge: http://image-net.org/ Visual Storytelling Dataset (VIST): http://visionandlanguage.net/VIST/ Wikipedia: https://www.wikipedia.org/ Microsoft COCO: http://cocodataset.org/#home

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