Fusion Models for Improved Visual Captioning

Visual captioning aims to generate textual descriptions given images. Traditionally, the 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 to often make 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 an 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.

Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods

Integration of vision and language tasks has seen a significant growth in the recent times due to surge of interest from multi-disciplinary communities such as deep learning, computer vision, and natural language processing. In this survey, we focus on ten different vision and language integration tasks in terms of their problem formulation, methods, existing datasets, evaluation measures, and comparison of results achieved with the corresponding state-of-the-art methods. This goes beyond earlier surveys which are either task-specific or concentrate only on one type of visual content i.e., image or video. We then conclude the survey by discussing some possible future directions for integration of vision and language research.