{"url":"/dataset/sesiv","name":"SESIV","full_name":"SEmantic Salient Instance Video","description_markdown":"SEmantic Salient Instance Video (SESIV) dataset is obtained by augmenting the DAVIS-2017 benchmark dataset by assigning semantic ground-truth for salient instance labels. The SESIV dataset consists of 84 high-quality video sequences with pixel-wisely per-frame ground-truth labels.\r\n\r\nSource: [Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation](https://arxiv.org/pdf/1807.01452)","description_withheld":null,"homepage":"https://sites.google.com/view/ltnghia/research/sesiv","introduced_date":"2018-07-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/semantic-instance-meets-salient-object-study","title":"Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation","first_author":"Trung-Nghia Le","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Robot Navigation","url":"/task/robot-navigation","datasets_with_task":"/datasets/task/robot-navigation"},{"name":"Self-Driving Cars","url":"/task/self-driving-cars","datasets_with_task":"/datasets/task/self-driving-cars"}],"languages":[],"variants":["SESIV"],"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."}