{"url":"/dataset/complex-tv-qa","name":"Complex-TV-QA","full_name":"complex traffic video question answer datset","description_markdown":"The Complex-TV-QA dataset, to our knowledge, is the inaugural resource that provides human-annotated, detailed video captions within traffic scenarios, alongside complex reasoning questions. This novel dataset not only stands as a vital tool for evaluating language models in real-world video-QA and video-reasoning research, but also offers valuable insights for the development and understanding of multi-modal video reasoning models and related works.","description_withheld":null,"homepage":"https://github.com/saccharomycetes/text-based-traffic-understanding","introduced_date":"2023-06-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-study-of-situational-reasoning-for-traffic","title":"A Study of Situational Reasoning for Traffic Understanding","first_author":"Jiarui Zhang","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Complex-TV-QA"],"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."}