Datasets › ViSal

ViSal

2 Sep 2016 archive 2025-07-28

DataViSal.rar (including the ground truth data) is our new collected dataset for the following paper.

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W. Wang, J. Shen, and L. Shao, Consistent video saliency using local gradient flow optimization and global refinement,
IEEE Trans. on Image Processing, 24(11):4185-4196, 2015
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The related source code can be downloaded from: https://github.com/shenjianbing/videosal

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Note:

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The data and code files are free to use for research purposes. If you use them for research purposes, you should cite above paper in any resulting publication.

This code also uses some publicly available functions.

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Contact Information

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Email:

wenguanwang@bit.edu.cn

shenjianbing@bit.edu.cn

  shenjianbingcg@gmail.com

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Video Salient Object Detection ViSal RealFlow S-Measure 0.962 Transforming Static Images Using Generative Models for... — 10 Compare

Papers archive 2025-07-28

10 shown of 10 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 10. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • ViSal

1 variant name, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections