Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
License information was derived automatically
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
The dataset includes data collected through a survey aimed to study how travel-related decisions of residents of the Puget Sound Region in Washington State have changed as a result of the COVID-19 pandemic. In the survey, we asked each respondent about their travel behavior before and during the pandemic, what they expect their future (after the pandemic) travel choices would look like, and several socio-economic and psychometric questions. We used Google Forms as our data collection platform. A PDF of the questionnaire and meta data which explains each column are included with the data file. The survey was advertised through the Facebook page of the UW Civil and Environmental Engineering Department, and was live for 14 days (June 26-July 9, 2020). Ads were run on Facebook, Instagram, Messenger, and other social media platforms owned by Facebook, and were set to be shown only to the residents of the Puget Sound region in Washington State (King, Snohomish, Kitsap and Pierce counties). As an incentive to participate, respondents were entered in a drawing for their choice of an Apple iPad or a Microsoft Surface tablet (retail price of about $400). The ads reached 49,146 people, of which 2,018 people (4.10%) clicked on the ad and opened the survey. Of the 2,018 people who clicked on the survey link, 1389 individuals completed the survey (68.83%). After data cleaning, we ended up with 1310 valid responses.
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Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
License information was derived automatically
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