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The size and the computational load of fine-tuning large-scale pre-trainedneural network are becoming two major obstacles in adopting machine learning inmany applications. Continual learning (CL) can serve as a remedy throughenabling knowledge-transfer across sequentially arriving tasks which relaxesthe need to fine-tune all network weights from scratch. However, existing CLalgorithms primarily consider learning unimodal vision-only or language-onlytasks. We develop a transformer-based CL architecture for learning bimodalvision-and-language tasks based on increasing the number of the learnableparameters dynamically and using knowledge distillation. The new additionalparameters are used to specialize the network for each task. Our approachenables sharing information between the tasks while addressing the challenge ofcatastrophic forgetting. Our approach is scalable learning to a large number oftasks because it requires little memory and time overhead. Our model reachesstate-of-the-art performance on challenging vision-and-language tasks.

Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation