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Semantic Augmentation in Images using Language
Deep Learning models are incredibly data-hungry and require very large labeled datasets for supervised learning.
Attribution Regularization for Multimodal Paradigms
This research project aims to address these challenges by proposing a novel regularization term that encourages multimodal models to effectively utilize information from all modalities when making decisions.
MAEA: Multimodal Attribution for Embodied AI
Understanding multimodal perception for embodied AI is an open question because such inputs may contain highly complementary as well as redundant information for the task.
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