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We Have So Much In Common: Modeling Semantic Relational Set Abstractions
  in Videos

We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos

European Conference on Computer Vision (ECCV), 2020
12 August 2020
A. Andonian
Camilo Luciano Fosco
Mathew Monfort
Allen Lee
Rogerio Feris
Carl Vondrick
A. Oliva
ArXiv (abs)PDFHTML

Papers citing "We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos"

3 / 3 papers shown
Transcript to Video: Efficient Clip Sequencing from Texts
Transcript to Video: Efficient Clip Sequencing from TextsACM Multimedia (ACM MM), 2021
Yu Xiong
Fabian Caba Heilbron
Dahua Lin
CLIP
251
14
0
25 Jul 2021
Abstraction and Analogy-Making in Artificial Intelligence
Abstraction and Analogy-Making in Artificial IntelligenceAnnals of the New York Academy of Sciences (Ann. N.Y. Acad. Sci.), 2021
Melanie Mitchell
410
198
0
22 Feb 2021
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action
  Video Understanding
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video UnderstandingIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
Mathew Monfort
Bowen Pan
K. Ramakrishnan
A. Andonian
Barry A. McNamara
A. Lascelles
Quanfu Fan
Dan Gutfreund
Rogerio Feris
A. Oliva
VLM
826
80
0
01 Nov 2019
1
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