{"url":"/dataset/viphy","name":"ViPhy","full_name":"“Visible” Physical Commonsense Knowledge","description_markdown":"**ViPhy** leverages two datasets: Visual Genome (Krishna et al., 2017), and ADE20K (Zhou et al., 2017). The dense captions in Visual Genome provide a broad coverage of object classes, making it a suitable resource for collecting subtype candidates. For extracting hyponyms from knowledge base, we acquire \"is-a\" relations from ConceptNet (Speer et al., 2017), and augment the subtype candidate set. We extract spatial relations from ADE20K, as it provides images categorised by scene type – primarily indoor environments with high object density: {bedroom, bathroom, kitchen, living room, office}.","description_withheld":null,"homepage":"https://github.com/axe--/viphy","introduced_date":"2022-09-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/viphy-probing-visible-physical-commonsense","title":"VIPHY: Probing \"Visible\" Physical Commonsense Knowledge","first_author":"Shikhar Singh","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["ViPhy"],"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."}