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Found 3 papers, 3 papers with code
DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible missing modalities at test time.
Cross-modal Learning by Hallucinating Missing Modalities in RGB-D Vision
We report state-of-the-art or comparable results on video action recognition on the largest multimodal dataset available for this task, the NTU RGB+D, as well as on the UWA3DII and Northwestern-UCLA.
Learning with privileged information via adversarial discriminative modality distillation
This raises the challenge of how to extract information from multimodal data in the training stage, in a form that can be exploited at test time, considering limitations such as noisy or missing modalities.
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