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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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Multimodal Machine Learning Applications
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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.

476 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.
476 works in the cohort · of 4,299,418page 1 of 10

Labels cover 3 of 476 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 476 of 476 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.

afffundunlabeled
FiLM: Visual Reasoning with a General Conditioning Layer
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, Aaron Courville
2018· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
1,633
citations
afffundunlabeled
Describing Videos by Exploiting Temporal Structure
Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle +1 more
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
956
citations
affunlabeled
Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Rich Zemel
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
568
citations
affunlabeled
Areas of Attention for Image Captioning
Marco Pedersoli, Thomas W. Lucas, Cordelia Schmid, Jakob Verbeek
2017· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
218
citations
fundno affunlabeled
SentiCap: Generating Image Descriptions with Sentiments
A. P. Mathews, Lexing Xie, Xuming He
2016· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
214
citations
affunlabeled
PlanIT
Kai Wang, Ben Weissmann, Manolis Savva, Anne Lynn S. Chang, Daniel Ritchie
2019· article· en· ACM Transactions on Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
207
citations
affunlabeled
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
Satya Krishna Gorti, Noël Vouitsis, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guangwei Yu
2022· article· en· 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)· Computer Science
machine prediction:candidate · noneconsensus · none
204
citations
affunlabeled
Streamlined Dense Video Captioning
Jonghwan Mun, Linjie Yang, Zhou Ren, Ning Xu, Bohyung Han
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
153
citations
affunlabeled
Semantic Grouping Network for Video Captioning
Hobin Ryu, Sunghun Kang, Haeyong Kang, Chang D. Yoo
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
148
citations
fundno affunlabeled
Modulating early visual processing by language
H. de Vries, Florian Strub, Hugo Larochelle, Olivier Pietquin
2017· article· en· LillOA (Université de Lille (University Of Lille))· Computer Science
machine prediction:candidate · noneconsensus · none
147
citations
affunlabeled
A Survey of Vision-Language Pre-Trained Models
Yifan Du, Zikang Liu, Junyi Li, Wayne Xin Zhao
2022· article· en· Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
136
citations
affunlabeled
Adversarial Generation of Natural Language
Sandeep Subramanian, Sai Rajeswar, Francis Dutil, Chris Pal, Aaron Courville
2017· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
115
citations
afffundunlabeled
Visual Relationship Detection With Deep Structural Ranking
Kongming Liang, Yuhong Guo, Hong Chang, Xilin Chen
2018· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
96
citations
affunlabeled
Order-Embeddings of Images and Language
Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun
2015· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
87
citations
afffundunlabeled
Learning Cross-Modal Context Graph for Visual Grounding
Yongfei Liu, Bo Wan, Xiaodan Zhu, Xuming He
2020· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
84
citations
affunlabeled
Generating Images from Captions with Attention
Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov
2015· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
75
citations
affunlabeled
PACO: Parts and Attributes of Common Objects
Vignesh Ramanathan, Anmol Kalia, Vladan Petrović, Yi Wen, Baixue Zheng, Baishan Guo +8 more
2023· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
62
citations

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