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Unsupervised Saliency Detection datasets

archive 2025-07-28

3 datasets carry the task tag "Unsupervised Saliency Detection" (the task itself: Unsupervised Saliency Detection), ordered by the archive's paper count. Page 1 of 1: 3 shown of 3. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

Unsupervised Saliency Detection datasets 1–3 of 3

DUTS is a saliency detection dataset containing 10,553 training images and 5,019 test images.
286 papers · 5 benchmarks
The DUT-OMRON dataset is used for evaluation of Salient Object Detection task and it contains 5,168 high quality images.
214 papers · 4 benchmarks
ECSSD (Extended Complex Scene Saliency Dataset)
The Extended Complex Scene Saliency Dataset (ECSSD) is comprised of complex scenes, presenting textures and structures common to real-world images.
34 papers · 5 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.