{"url":"/dataset/gebid","name":"GeBiD","full_name":"Geometric shapes Bimodal Dataset","description_markdown":"We provide a custom synthetic bimodal dataset, called GeBiD, designed specifically for the comparison of the joint- and cross-generative capabilities of Multimodal Variational Autoencoders. It comprises RGB images of geometric primitives and textual descriptions.  The dataset offers 5 levels of difficulty (based on the number of attributes) to find the minimal functioning scenario for each model. Moreover, its rigid structure enables automatic qualitative evaluation of the generated samples.","description_withheld":null,"homepage":"https://github.com/gabinsane/multimodal-vae-comparison","introduced_date":"2022-09-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarking-multimodal-variational","title":"Benchmarking Multimodal Variational Autoencoders: CdSprites+ Dataset and Toolkit","first_author":"Gabriela Sejnova","url":null},"license":{"name":"CC BY-NC-SA 4.0 license","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Multimodal Deep Learning","url":"/task/multimodal-deep-learning","datasets_with_task":"/datasets/task/multimodal-deep-learning"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["GeBiD"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}