{"url":"/dataset/streamingbench","name":"StreamingBench","full_name":null,"description_markdown":"StreamingBench evaluates Multimodal Large Language Models (MLLMs) in real-time, streaming video understanding tasks. 🌟\r\n\r\n🎞️ Overview\r\nAs MLLMs continue to advance, they remain largely focused on offline video comprehension, where all frames are pre-loaded before making queries. However, this is far from the human ability to process and respond to video streams in real-time, capturing the dynamic nature of multimedia content. To bridge this gap, StreamingBench introduces the first comprehensive benchmark for streaming video understanding in MLLMs.\r\n\r\nKey Evaluation Aspects\r\n🎯 Real-time Visual Understanding: Can the model process and respond to visual changes in real-time?\r\n🔊 Omni-source Understanding: Does the model integrate visual and audio inputs synchronously in real-time video streams?\r\n🎬 Contextual Understanding: Can the model comprehend the broader context within video streams?\r\nDataset Statistics\r\n📊 900 diverse videos\r\n📝 4,500 human-annotated QA pairs\r\n⏱️ Five questions per video at different timestamps","description_withheld":null,"homepage":"https://huggingface.co/datasets/mjuicem/StreamingBench","introduced_date":"2024-11-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/streamingbench-assessing-the-gap-for-mllms-to","title":"StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding","first_author":"Junming Lin","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["StreamingBench"],"data_loaders":[],"num_papers_in_archive":5,"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":"2026-09-25T09:33:49+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."}