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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 3 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
Self-Supervised Attention-Aware Reinforcement Learning
Haiping Wu, Khimya Khetarpal, Doina Precup
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
Visual Attention Retargeting
Victor A. Mateescu, Ivan V. Bajić
2015· article· en· IEEE Multimedia· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
fundno affunlabeled
A neuromorphic system for video object recognition
Deepak Khosla, Yang Chen, Kyungnam Kim
2014· article· en· Frontiers in Computational Neuroscience· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affno abstractunlabeled
Holistic and Deep Feature Pyramids for Saliency Detection.
Shizhong Dong, Zhifan Gao, Shanhui Sun, Xin Wang, Ming Li, Heye Zhang +3 more
2018· article· en· British Machine Vision Conference· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
afffundunlabeled
A Focus on Selection for Fixation
John K. Tsotsos, Iuliia Kotseruba, Calden Wloka
2016· article· en· Journal of Eye Movement Research· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
afffundunlabeled
The art of gaze guidance.
Nida Latif, Arlene Gehmacher, Monica S. Castelhano, Kevin G. Munhall
2013· article· en· Journal of Experimental Psychology Human Perception & Performance· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
A Skill-Based Visual Attention Model for Cloud Gaming
Hamed Ahmadi, Saman Zadtootaghaj, Farhad Pakdaman, Mahmoud Reza Hashemi, Shervin Shirmohammadi
2021· article· en· IEEE Access· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
Where2Stand
Yinting Wang, Mingli Song, Dacheng Tao, Yong Rui, Jiajun Bu, Ah Chung Tsoi +2 more
2015· article· en· ACM Transactions on Intelligent Systems and Technology· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
16
citations
affno abstractunlabeled
Object Detection without Color Feature: Case Study Autonomous Robot
Deshinta Arrova Dewi, Elankovan Sundararajan, Anton Satria Prabuwono, Lau Meng Cheng
2019· article· en· International Journal of Mechanical Engineering and Robotics Research· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
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