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Poster
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning
Riccardo Del Chiaro · Bartłomiej Twardowski · Andrew Bagdanov · Joost van de Weijer

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #764
in Poster Session 4 »

Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper we take a systematic look at continual learning of LSTM-based models for image captioning. We propose an attention-based approach that explicitly accommodates the transient nature of vocabularies in continual image captioning tasks -- i.e. that task vocabularies are not disjoint. We call our method Recurrent Attention to Transient Tasks (RATT), and also show how to adapt continual learning approaches based on weight regularization and knowledge distillation to recurrent continual learning problems. We apply our approaches to incremental image captioning problem on two new continual learning benchmarks we define using the MS-COCO and Flickr30 datasets. Our results demonstrate that RATT is able to sequentially learn five captioning tasks while incurring no forgetting of previously learned ones.

Author Information

Riccardo Del Chiaro (University of Florence)
Bartłomiej Twardowski (Computer Vision Center, UAB)
Andrew Bagdanov (University of Florence)
Joost van de Weijer (Computer Vision Center Barcelona)

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