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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

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The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Visual Attention and Saliency Detection
Retraction
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

509 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
509 works in the cohort · of 4,299,418page 1 of 11

Labels cover 1 of 509 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 509 of 509 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affunlabeled
Global contrast based salient region detection
Ming‐Ming Cheng, Guoxin Zhang, Niloy J. Mitra, Xiaolei Huang, Shi‐Min Hu
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3,095
citations
affunlabeled
BASNet: Boundary-Aware Salient Object Detection
Xuebin Qin, Zichen Zhang, Chenyang Huang, Chao Gao, Masood Dehghan, Martin Jägersand
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1,565
citations
afffundno abstractunlabeled
Controlling low-level image properties: The SHINE toolbox
Verena Willenbockel, Javid Sadr, Daniel Fiset, Greg O. Horne, Frédéric Gosselin, James W. Tanaka
2010· article· en· Behavior Research Methods· Computer Science
machine prediction:candidate · noneconsensus · none
1,232
citations
affunlabeled
Saliency Based on Information Maximization
Neil D. B. Bruce, John K. Tsotsos
2005· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
1,100
citations
affunlabeled
Saliency-Aware Video Compression
Hadi Hadizadeh, Ivan V. Bajić
2013· article· en· IEEE Transactions on Image Processing· Computer Science
machine prediction:candidate · noneconsensus · none
328
citations
fundno affno abstractunlabeled
Intrinsic and extrinsic effects on image memorability
Zoya Bylinskii, Phillip Isola, Constance M. Bainbridge, Antonio Torralba, Aude Oliva
2015· article· en· Vision Research· Computer Science
machine prediction:candidate · noneconsensus · none
276
citations
afffundunlabeled
Calibrated RGB-D Salient Object Detection
Wei Ji, Jingjing Li, Shuang Yu, Miao Zhang, Yongri Piao, Shunyu Yao +5 more
2021· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
262
citations
affunlabeled
Video Panoptic Segmentation
Dahun Kim, Sanghyun Woo, Joon‐Young Lee, In So Kweon
2020· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
163
citations
fundno affunlabeled
Novelty Is Not Always the Best Policy
Michael D. Dodd, Stefan Van der Stigchel, Andrew Hollingworth
2009· article· en· Psychological Science· Computer Science
machine prediction:candidate · noneconsensus · none
74
citations
affunlabeled
On saliency, affect and focused attention
Lori McCay‐Peet, Mounia Lalmas, Vidhya Navalpakkam
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
72
citations
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