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We introduce a novel Distillation Multiple Choice Learning framework for multimodal data, where different modality networks learn in a cooperative setting from scratch, strengthening one another. ... We evaluate this approach on three video action recognition benchmark datasets. We obtain state-of-the-art results in comparison to other approaches that work with missing modalities at test time. ... petitive to or state-of-the-art results compared to the privileged information literature, and significantly higher accuracy compared to independently trained modality networks for human action recognition ...arXiv:1912.10982v1 fatcat:aplmcrqnufai7mjrf4rgqzcw2u
To my favorite person in the world, Patrícia, for the unique care, understanding and closeness. To Vanessa, for all the love, patience, and support. ... Sclaroff, "DMCL: Distillation Multiple Choice Learning", under revision, 2019. ... Distillation Multiple Choice Learning (DMCL) allows multiple modalities to cooperate and strengthen one another. ...doi:10.15167/da-cruz-garcia-nuno-ricardo_phd2020-02-28 fatcat:gndajfrfxnbplle6kqhdnl67yi