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pro vyhledávání: '"Schiappa, Madeline C."'
In this work, we focus on generating graphical representations of noisy, instructional videos for video understanding. We propose a self-supervised, interpretable approach that does not require any annotations for graphical representations, which wou
Externí odkaz:
http://arxiv.org/abs/2207.08001
Publikováno v:
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (2022)
Joint visual and language modeling on large-scale datasets has recently shown good progress in multi-modal tasks when compared to single modal learning. However, robustness of these approaches against real-world perturbations has not been studied. In
Externí odkaz:
http://arxiv.org/abs/2207.02159
Publikováno v:
ACM Comput. Surv. (December 2022)
The remarkable success of deep learning in various domains relies on the availability of large-scale annotated datasets. However, obtaining annotations is expensive and requires great effort, which is especially challenging for videos. Moreover, the
Externí odkaz:
http://arxiv.org/abs/2207.00419
Autor:
SCHIAPPA, MADELINE C.1 madelineschiappa@knights.ucf.edu, RAWAT, YOGESH S.1 yogesh@crcv.ucf.edu, SHAH, MUBARAK1 shah@crcv.ucf.edu
Publikováno v:
ACM Computing Surveys. 2023 Suppl13s, Vol. 55, p1-37. 37p.
