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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.

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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Advanced Image and Video Retrieval Techniques
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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.

1,044 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.
1,044 works in the cohort · of 4,299,418page 1 of 21

Labels cover 2 of 1,044 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 1,044 of 1,044 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.

afffundno abstractunlabeled
LabelMe: A Database and Web-Based Tool for Image Annotation
Bryan Russell, Antonio Torralba, Kevin Murphy, William T. Freeman
2007· article· en· International Journal of Computer Vision· Computer Science
machine prediction:candidate · noneconsensus · none
4,205
citations
affno abstractunlabeled
Pictorial Structures for Object Recognition
Pedro F. Felzenszwalb, Daniel P. Huttenlocher
2004· article· en· International Journal of Computer Vision· Computer Science
machine prediction:candidate · noneconsensus · none
2,199
citations
affno abstractunlabeled
Transforming Auto-Encoders
Geoffrey E. Hinton, Alex Krizhevsky, Sida D. Wang
2011· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1,323
citations
afffundno abstractunlabeled
Semantic hashing
Ruslan Salakhutdinov, Geoffrey E. Hinton
2008· article· en· International Journal of Approximate Reasoning· Computer Science
machine prediction:candidate · noneconsensus · none
1,272
citations
affunlabeled
Recognising panoramas
Brown, Lowe
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
681
citations
affunlabeled
Learning to Find Good Correspondences
Kwang Moo Yi, Eduard Trulls, Y. Ono, Vincent Lepetit, Mathieu Salzmann, Pascal Fua
2018· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
546
citations
affunlabeled
Hamming Distance Metric Learning
Mohammad Norouzi, David J. Fleet, Ruslan Salakhutdinov
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
518
citations
affno abstractunlabeled
50 Years of object recognition: Directions forward
Alexander Andreopoulos, John K. Tsotsos
2013· article· en· Computer Vision and Image Understanding· Computer Science
machine prediction:candidate · noneconsensus · none
351
citations
affunlabeled
Cartesian K-Means
Mohammad Norouzi, David J. Fleet
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
309
citations
affunlabeled
Fast Matching of Binary Features
Marius Muja, David Lowe
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
291
citations
affunlabeled
Picking the best DAISY
Simon Winder, Gang Hua, Matthew A. Brown
2009· article· en· 2009 IEEE Conference on Computer Vision and Pattern Recognition· Computer Science
machine prediction:candidate · noneconsensus · none
260
citations
affunlabeled
MixVPR: Feature Mixing for Visual Place Recognition
Amar Ali-bey, Brahim Chaib-draa, Philippe Giguère
2023· article· en· 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)· Computer Science
machine prediction:candidate · noneconsensus · none
226
citations
affunlabeled
SHREC '11: Robust Feature Detection and Description Benchmark
Edmond Boyer, Alexander M. Bronstein, Michael M. Bronstein, Benjamín Bustos, T. Darom, Radu Horaud +12 more
2011· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
209
citations
affno abstractunlabeled
Benchmarking Image Segmentation Algorithms
Francisco José Pozuelos Estrada, Allan D. Jepson
2009· article· en· International Journal of Computer Vision· Computer Science
machine prediction:candidate · noneconsensus · none
184
citations
affunlabeled
Scalable Image Coding for Humans and Machines
Hyomin Choi, Ivan V. Bajić
2022· article· en· IEEE Transactions on Image Processing· Computer Science
machine prediction:candidate · noneconsensus · none
148
citations
affunlabeled
Indexing hierarchical structures using graph spectra
Ali Shokoufandeh, Diego Macrini, Sven Dickinson, Kaleem Siddiqi, Steven W. Zucker
2005· article· en· IEEE Transactions on Pattern Analysis and Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
140
citations
afffundaboutunlabeled
LSH ensemble
Erkang Zhu, Fatemeh Nargesian, Ken Q. Pu, Renée J. Miller
2016· article· en· Proceedings of the VLDB Endowment· Computer Science
machine prediction:candidate · noneconsensus · none
136
citations
afffundunlabeled
Using the forest to see the trees
Antonio Torralba, Kevin Murphy, William T. Freeman
2010· article· en· Communications of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
129
citations

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