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Vid2Seq: Large-Scale Pretraining of a Visual Language Model..:
, In:
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
,
Yang, Antoine
;
Nagrani, Arsha
;
Seo, Paul Hongsuck
... - p. 10714-10726 , 2023
Link:
https://doi.org/10.1109/CVPR52729.2023.01032
RT T1 <cTitle>2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</cTitle>: T1 <Title>Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10203714&Exemplar=1&LAN=DE A1 Yang, Antoine A1 Nagrani, Arsha A1 Seo, Paul Hongsuck A1 Miech, Antoine A1 Pont-Tuset, Jordi A1 Laptev, Ivan A1 Sivic, Josef A1 Schmid, Cordelia YR 2023 SN 2575-7075 K1 Location awareness K1 Visualization K1 Training data K1 Benchmark testing K1 Predictive models K1 Question answering (information retrieval) K1 Pattern recognition K1 Vision K1 language K1 reasoning SP 10714 OP 10726 LK http://dx.doi.org/https://doi.org/10.1109/CVPR52729.2023.01032 DO https://doi.org/10.1109/CVPR52729.2023.01032 SF ELIB - SuUB Bremen
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