{"url":"/dataset/visalign","name":"VisAlign","full_name":null,"description_markdown":"**VisAlign** is a dataset for measuring AI-human visual alignment in terms of image classification, a fundamental task in machine perception. In order to evaluate AI-Human visual alignment, a dataset should encompass samples with various scenarios that may arise in the real world and have gold human perception labels. The dataset consists of three groups of samples, namely Must-Act (i.e., Must-Classify), Must-Abstain, and Uncertain, based on the quantity and clarity of visual information in an image and further divided into eight categories.","description_withheld":null,"homepage":"https://github.com/jiyounglee-0523/VisAlign","introduced_date":"2023-08-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/visalign-dataset-for-measuring-the-degree-of","title":"VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception","first_author":"Jiyoung Lee","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[],"languages":[],"variants":["VisAlign"],"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."}