  Visual captioning aims to generate textual descriptions given images or
videos. Traditionally, image captioning models are trained on human annotated
datasets such as Flickr30k and MS-COCO, which are limited in size and
diversity. This limitation hinders the generalization capabilities of these
models while also rendering them liable to making mistakes. Language models
can, however, be trained on vast amounts of freely available unlabelled data
and have recently emerged as successful language encoders and coherent text
generators. Meanwhile, several unimodal and multimodal fusion techniques have
been proven to work well for natural language generation and automatic speech
recognition. Building on these recent developments, and with the aim of
improving the quality of generated captions, the contribution of our work in
this paper is two-fold: First, we propose a generic multimodal model fusion
framework for caption generation as well as emendation where we utilize
different fusion strategies to integrate a pretrained Auxiliary Language Model
(AuxLM) within the traditional encoder-decoder visual captioning frameworks.
Next, we employ the same fusion strategies to integrate a pretrained Masked
Language Model (MLM), namely BERT, with a visual captioning model, viz. Show,
Attend, and Tell, for emending both syntactic and semantic errors in captions.
Our caption emendation experiments on three benchmark image captioning
datasets, viz. Flickr8k, Flickr30k, and MSCOCO, show improvements over the
baseline, indicating the usefulness of our proposed multimodal fusion
strategies. Further, we perform a preliminary qualitative analysis on the
emended captions and identify error categories based on the type of
corrections.
