{"url":"/dataset/visal","name":"ViSal","full_name":null,"description_markdown":"DataViSal.rar (including the ground truth data) is our new collected dataset for the following paper.\r\n\r\n===========================================================================\r\n\r\nW. Wang, J. Shen, and L. Shao, \r\nConsistent video saliency using local gradient flow optimization and global refinement,  \r\nIEEE Trans. on Image Processing, 24(11):4185-4196, 2015  \r\n===========================================================================\r\n\r\nThe related source code can be downloaded from:\r\nhttps://github.com/shenjianbing/videosal\r\n\r\n===========================================================================\r\n\r\nNote:\r\n\r\n===========================================================================\r\n\r\nThe data and code files are free to use for research purposes. \r\nIf you use them for research purposes,\r\nyou should cite above paper in any resulting publication.\r\n\r\nThis code also uses some publicly available functions.\r\n\r\n===========================================================================\r\n\r\nContact Information\r\n\r\n===========================================================================\r\n\r\nEmail:\r\n\r\n    wenguanwang@bit.edu.cn\r\n\r\n\tshenjianbing@bit.edu.cn\r\n\r\n\t  shenjianbingcg@gmail.com","description_withheld":null,"homepage":"https://github.com/shenjianbing/ViSalDataset","introduced_date":"2016-09-02","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Video Salient Object Detection","url":"/task/video-salient-object-detection","datasets_with_task":"/datasets/task/video-salient-object-detection"}],"languages":[],"variants":["ViSal"],"data_loaders":[],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-salient-object-detection-on-visal","task":"Video Salient Object Detection","dataset_variant":"ViSal","rows":10,"metrics":["S-Measure","max E-measure","Average MAE"],"first_row_in_archive_order":{"model":"RealFlow","paper":"/paper/transforming-static-images-using-generative","metrics":{"Average MAE":"0.010","S-Measure":"0.962","max E-measure":"0.966"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/transforming-static-images-using-generative","title":"Transforming Static Images Using Generative Models for Video Salient Object Detection","date":"2024-11-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-unified-transformer-framework-for-group","title":"A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection","date":"2022-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":0,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/shifting-more-attention-to-video-salient","title":"Shifting More Attention to Video Salient Object Detection","date":"2019-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-video-object-segmentation-with-1","title":"Unsupervised Video Object Segmentation with Motion-based Bilateral Networks","date":"2018-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/pyramid-dilated-deeper-convlstm-for-video","title":"Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection","date":"2018-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/flow-guided-recurrent-neural-encoder-for","title":"Flow Guided Recurrent Neural Encoder for Video Salient Object Detection","date":"2018-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/real-time-salient-object-detection-with-a","title":"Real-Time Salient Object Detection With a Minimum Spanning Tree","date":"2016-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/minimum-barrier-salient-object-detection-at","title":"Minimum Barrier Salient Object Detection at 80 FPS","date":"2015-12-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/saliency-aware-geodesic-video-object","title":"Saliency-Aware Geodesic Video Object Segmentation","date":"2015-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/time-mapping-using-space-time-saliency","title":"Time-Mapping Using Space-Time Saliency","date":"2014-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":13,"samples_ran":0,"samples_unverified":13,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}