Memory Based Attentive Fusion

16 Jul 2020Darshana PriyasadTharindu FernandoSimon DenmanSridha SridharanClinton Fookes

The use of multi-modal data for deep machine learning has shown promise when compared to uni-modal approaches, where fusion of multi-modal features has resulted in improved performance. However, most state-of-the-art methods use naive fusion which processes feature streams from a given time-step and ignores long-term dependencies within the data during fusion... (read more)

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