{"url":"/dataset/lsdbench","name":"LSDBench","full_name":"Long-video Sampling Dilemma Benchmark","description_markdown":"A benchmark that focuses on the sampling dilemma in long-video tasks. The LSDBench dataset is designed to evaluate the sampling efficiency of long-video VLMs. It consists of multiple-choice question-answer pairs based on hour-long videos, focusing on dense and short-duration actions with high Necessary Sampling Density (NSD).","description_withheld":null,"homepage":"https://github.com/dvlab-research/LSDBench","introduced_date":"2025-03-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/does-your-vision-language-model-get-lost-in","title":"Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?","first_author":"Tianyuan Qu","url":null},"license":{"name":"Apache License, Version 2.0","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"},{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Actions","url":"/datasets/modality/actions"}],"tasks":[{"name":"Video Understanding","url":"/task/video-understanding","datasets_with_task":"/datasets/task/video-understanding"},{"name":"Video Segmentation","url":"/task/video-segmentation","datasets_with_task":"/datasets/task/video-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LSDBench"],"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."}