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Consistent multiple sequence decoding

University of British Columbia

Sequence decoding is one of the core components of most visual-lingual models. However, typical neural decoders when faced with decoding multiple, possibly correlated, sequences of tokens resort to simple independent decoding schemes. In this work, we introduce a consistent multiple sequence decoding architecture, which, while relatively simple, is general and allows for consistent and simultaneous decoding of an arbitrary number of sequences. Our formulation utilizes a consistency fusion mechanism, implemented using message passing in a Graph Neural Network (GNN), to aggregate context from related decoders. This context is then utilized as a secondary input, in addition to previously generated output, to make a prediction at a given step of decoding. Self-attention, in the GNN, is used to modulate the fusion mechanism locally at each node and each step in the decoding process. We show the efficacy of our consistent multiple sequence decoder on the task of dense relational captioning and illustrate state-of-the-art performance (improvement of 5.2% in mAP) on the task. More importantly, we illustrate that the decoded sentences, for the same regions, are more consistent (improvement of 9.5% in consistency score), while across images and regions maintain diversity.

Text

2020-08-11

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/75418

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/